The field of temporal image forensics is the science of exploiting age-dependent traces introduced by the image acquisition pipeline to approximate the age of a digital image relative to images from the same device. This task can be viewed as a classification problem, where the classes are defined by the temporal resolution of the considered age traces and the available images. It has already been shown that applying a conventional deep neural network (i.e., an image classifier) to this classification problem leads to unreliable predictions. A main reason for this is the content bias inherent in the data. Models from the field of image steganalysis usually include a preprocessing layer, which increases the signal-to-noise ratio (suppresses image content) by generating high-pass filter residuals. Similar to the stego signal, age traces (in-field sensor defects) are also high-frequency image components ‘hidden’ in an image. Thus, are steganalysis models suitable for temporal image forensics? Multiple experiments are conducted to investigate this question. In principle, when comparing steganalysis models with generic image classifiers, steganalysis models do not exhibit a better ability to detect weak age traces. Although image content is suppressed by the preprocessing layer, the models are still prone to content bias.

Four distinct GAN-based I2I translation techniques are employed for the synthesis of biometric finger vein presentation attack instrument (PAI) samples corresponding to three public presentation attack datasets. The PAD training using these synthetic PAI samples reveals weaknesses for a small share of settings (in terms of datasets and GAN types). Removing the GAN model fingerprints (with three technical variants) from the synthetic data is not resolving these problematic PAD results, in contrary, this strategy creates more problematic results than it was intended to resolve. Finally, we show that PAI samples generated with different GAN types can be easily discriminated, even when using identical GAN types but only different parameter setups the resulting synthetic data can still be differentiated. This indicates that the present GAN model fingerprints are stronger than often believed, eventually caused by the significant redundancy present in our biometric datasets as compared to natural data as typically used in GAN fingerprint assessments. Overall, in the generation of synthetic PAI samples, CycleGAN as well as StarGANv2 generated specimens turn out to be highly useful to train finger vein PAD systems.

This work investigates the possibility to conduct a forensic discrimination of decoded versions of 10 different lossy image compression file formats, including 4 ISO/IEC still image compression standards (JPEG, JPEG 2000, JPEG XR, JPEG XL) and 4 video-coding related image compression schemes (AVIF, HEIC, BPG, WEBP). We have found that a proper compression artefact discrimination can be achieved across different compression ratios by fine-tuning a standard ResNet-18 model using a variety of different file sizes in training. Classification accuracy is almost perfect for low quality image data (as compression artefacts are strong), while the 10-class discrimination accuracy is slightly beyond 85% for high quality imagery which can be considered almost visually lossless. Observed mis-classifications are mostly along the lines of expectations due to algorithmic differences and similarities (block-size, transform type, etc.), only JPEG 2000 exhibits some unexpected artefact similarities to JPEG XR when the photo overlap transform is being employed.

In literature about selective encryption of image and video content, image quality indices are usually used to gauge the degree of encryption. These methods have frequently been shown not to work well for the evaluation of encryption, mainly due to them being trained on predominantly high quality contents. The problem for creating a proper recognition index or visual encryption strength index is the lack of data to train on. In this paper we present the first database of encrypted video content, ranging from high quality to completely unrecognizable, together with human observer scores for quality and recognizability. We also provide a basic evaluation of visual quality indices on this database, directly and in different combination by fusion, to showcase that currently image and video quality indices are ill fit for the purpose of estimating video encryption strength/recognizably. This inability of quality indices to perform also showcases that this database fills the required blind spot of currently available data.

In the context of temporal image forensics, it is not evident that a neural network, trained on images from different time-slots (classes), exploits solely image age related features. Usually, images taken in close temporal proximity (e.g., belonging to the same age class) share some common content properties. Such content bias can be exploited by a neural network. In this work, a novel approach is proposed that evaluates the influence of image content. This approach is verified using synthetic images (where content bias can be ruled out) with an age signal embedded. Based on the proposed approach, it is shown that a deep learning approach proposed in the context of age classification is most likely highly dependent on the image content. As a possible countermeasure, two different models from the field of image steganalysis, along with three different preprocessing techniques to increase the signal-to-noise ratio (age signal to image content), are evaluated using the proposed method.

Several attempts have been made recently to generate synthetic fingerprint data. This has become necessary after legal changes in Europe and some US states in order to allow and continue long-term developments in the field of fingerprint biometrics. Apart from utilizing traditional methods (often based on Gabor filters), deep convolutional neural networks are widely used to generate synthetic fingerprint samples. The current study aims at comparing several publicly available synthetic fingerprint datasets with several datasets that consist of imprints taken from real people. To enable a comparison, first a detailed description of these datasets is carried out. Secondly, an available 4-level protocol is used, which is supposed to show similarities and/or differences between real and synthetic fingerprint samples in terms of quality assessment and non-mated as well as mated comparison scores’ behavior. Furthermore, a new synthetic FP dataset composed of 50k samples is created and made publicly available in the course of this study.

Recently, there is a growing interest to employ biometrics in post-mortem forensics, mainly to replace cost intensive radiology based imaging devices. While it has been shown that post-mortem biometric recognition is feasible for fingerprints, face and iris, no studies regarding post-mortem vasculature pattern recognition have been published. Based on the first reported post-mortem hand- and finger-vein dataset, the hypothesis, that hand vasculature biometrics can be used as post-mortem biometric modality, is falsified. Using an indirect proof, it is shown that no usable vascular features are present in the small amount of sample data collected, by visual inspection as well as by applying several biometric quality metrics, which confirm that hand-based vasculature biometrics can not be used as post-mortem biometric modality.

A UNet-type encoder-decoder inpainting network is applied to weaken the protection strength of selectively encrypted face samples. Based on visual assessment, FaceQNet quality, and ArcFace recognition accuracy the strategy is shown to be successful, however, to a different extent depending on the original protection strength. For almost cryptographic strength, inpainting does not cause a practically relevant protection weakening, while for lower original protection strength inpainting almost removes the protection entirely.

I2I translation techniques for unpaired data are used for the creation of biometric presentation attack artefact samples. For the assessment of these synthetic samples, we analyse their behaviour when attacking hand vein recognition systems, comparing these results to such obtained from actually crafted presentation attack samples. We observe that although visual appearance and sample set correspondence are suprisingly good, respectively, the assessment of the behaviour of the data in a conducted attack is more difficult. Even if for some recognition schemes we find a good accordance in terms of IAPMR (for others we don't), the attack score distributions turn out to be highly dissimilar. More work is needed for reliable assesment of such data, to be able to correctly interpret corresponding results with respect to the usefulness in attack simulation.

In this paper, we provide an overview of techniques for human subject identification from biomedical signals, highlighting the potential threat for patient privacy considering public repositories of medical data. After an in-depth review of lesser known approaches, we conclude that performing a disentanglement and elimination of the identity related attributes from the medical image data is a potential solution for this problem.

Even with trained operators and cooperative subjects, it is still possible to capture off-angle iris images. Considering the recent demands for stand-off iris biometric systems and the trend towards "on-the-move-acquisition", off-angle iris recognition became a hot topic within the biometrics community. In this work, CNNs trained with the triplet loss function are applied to extract features for iris recognition. To analyze which parts of the eye are most suited for the CNN-based recognition system, experiments are carried out using image data from different parts of the eye (full eye, eye zoomed to iris, iris only, iris normalized, eye without iris). To analyze the impact of different gaze angles on the recognition performance, experiments are applied on: (1) different gaze angles separately, (2) image data with increasing differences in the gaze angles, and (3) corrected off-angle image data. The experiment results show superior performance of the CNN trained with the triplet loss on the iris images with more lateral gaze angles (≥ 30°). However, higher differences in the gaze angles between images deteriorate the network performance. Also, the results are about the same for the different parts of the eye and correcting the gaze angle did not really improve the performance of the CNN.

When storing face biometric samples in accordance with ISO/IEC 19794 as JPEG2000 encoded images, it is necessary to encrypt them for the sake of users’ privacy. Literature suggests selective encryption of JPEG2000 images as fast and efficient method for encryption, the trade-off is that some information is left in plaintext. This could be used by an attacker, in case the encrypted biometric samples are leaked. In this work, we will attempt to utilize a convolutional neural network to perform cryptanalysis of the encryption scheme. That is, we want to assess if there is any information left in plaintext in the selectively encrypted face images which can be used to identify the person. The chosen approach is to train CNNs for biometric face recognition not only with plaintext face samples but additionally conduct a refinement training with partially encrypted data. If this system can successfully utilize encrypted face samples for biometric matching, we can show that the information left in encrypted biometric face samples is information actually usable for biometric recognition.The method works and we can show that a supposedly secure biometric sample still contains identifying information on average over the whole database.

The proof of origin of wood logs is becoming more and more important. In the context of Industry 4.0 and to combat illegal logging, there is an increased interest to track each individual log. In order to track roundwood from the forest to the sawmill, this work applies log recognition based on log end images from 100 logs that were captured first in the forest and later at the sawmill. The log images are segmented from the background, then preprocessed using a novel filtering approach and features are extracted using two CNN-based methods. In this work we show that using filtering approaches that improve the visibility of the annual ring pattern and suppress unwanted image information like the saw cut pattern clearly improve the recognition results.

Assessment of visual encryption of video and image content requires a reliable estimation of content recognizability and low quality. As pointed out in the literature, current methods are insufficient and research into this topic, as well as into the relation between low quality and recognizability, is still lacking. This lack of research is primarily due to a lack of data. To improve on the status-quo we have taken a recognizability database and performed a subjective quality evaluation on a subset of the images. This gives us a new database with both subjective recognizability and quality information and allows to delve into the relation between low quality and recognizability. We analyze the relationship between quality and recognizability as well as the predictive quality of state of the art visual quality indices. We show that the visual quality indices are poor indicators for the estimation of recognizability. Furthermore, we show that they must be a poor fit because of the disparity between two distinct perceptual tasks: quality and recognizability.

When biometric databases grow larger, a security breach or leak can affect millions. In order to protect against such a threat, the use of encryption is a natural choice. However, a biometric identification attempt then requires the decryption of a potential huge database, making a traditional approach potentially unfeasible. The use of selective JPEG2000 encryption can reduce the encryption’s computational load and enable a secure storage of biometric sample data. In this paper we will show that selective encryption of face biometric samples is secure. We analyze various encoding settings of JPEG2000, selective encryption parameters on the "Labeled Faces in the Wild" database and apply several traditional and deep learning based face recognition methods.

For the first time, the feasibility of creating morphed samples for attacking vascular biometrics is investigated, in particular finger vein recognition schemes are addressed. A conducted vulnerability analysis reveals that (i) the extent of vulnerability, (ii) the type of most vulnerable recognition scheme, and (iii) the preferred way to determine the best morph sample for a given target sample depends on the employed sensor. Digital morphs represent a significant threat as vulnerability in terms of IAPMR is often found to be > 0.8 or > 0.6 (in sensor dependent manner). Physical artefacts created from these morphs lead to clearly lower vulnerability (with IAPMR ≤ 0.25), however, this has to be attributed to the low quality of the artefacts (and is expected be increase for better artefact quality).

The act of presenting a forged biometric sample to a bio-metric capturing device is referred to as presentation at-tack. During the last decade this type of attack has been addressed for various biometric traits and is still a widely researched topic. This study follows the idea from a previously published work which employs the usage of twelve algorithms for finger vein recognition in order to perform an extensive vulnerability analysis on a presentation at-tack database. The present work adopts this idea and examines two already existing finger vein presentation attack databases with the goal to evaluate how hazardous these presentation attacks are from a wider perspective. Additionally, this study shows that by combining the matching scores from different algorithms, presentation attack detection can be achieved.

2D contactless to contact-based fingerprint (FP) comparison is a challenging task due to different types of distortion introduced during the capturing process. While contact-based FPs typically exhibit a wide range of elastic distortions, perspective distortions pose a problem in contactless FP imagery. In this work, we investigate three simple parametric warping models for contactless fingerprints — circular, elliptical and bidirectional warping — and show that these models can be used to improve the interoperability between the two modalities by simulating unfolding of a generic 3D model. Additionally, we employ score fusion as a technique to enhance the comparison performance in scenarios where multiple contactless FPs of the same finger are available. Using the simple circular warping, we have been able to decrease the Equal Error Rate (EER) from 1.79% to 0.78% and 1.82% to 1.31% on our dataset, respectively.

Image sensor defects that develop in field over a camera’s lifetime are at the core of temporal image forensics, as by knowing their onset time a temporal order can be assigned among pieces of evidence. In this context, only defects that have developed within the time interval of the available data set are relevant. The available methods for defect detection, based on regular scene images, aim to identify all present defects (e.g., to conceal them). In this paper, we introduce two novel defect detection techniques. Because of their properties, these methods only detect defects relevant for image age approximation. This is important since defects that do not provide additional age information can negatively affect the process of image age approximation.

Security and privacy is of great interest in biometric systems which can be offered by Match-on-Card (MoC) technology, successfully applied in several areas of biometrics. In finger vein recognition such a system is not available yet. Utilizing minutiae points from vein images in combination with classical minutiae-based fingerprint comparison software offers a great opportunity to integrate vein recognition on MoC systems. In this work a publicly available and two commercial fingerprint comparison tools are used to evaluate the recognition performance of vein minutiae, represented in a standardized data format, on three publicly available databases. The results strongly indicate that minutiae-based comparison technology from fingerprint recognition can be applied to finger vein recognition and is able to compete with and even outperform classical correlation-based methods utilized in this field. The work done here prepares the way for vein recognition on MoC systems.

As deepfakes become harder to detect by humans, more reliable detection methods are required to fight the spread of fake images and videos. In our work, we focus on PRNU-based detection methods, which, while popular in the image forensics scene, have not been given much attention in the context of deepfake detection. We adopt a PRNU-based approach originally developed for the detection of face morphs and facial retouching, and performed the first large scale test of PRNU-based deepfake detection methods on a variety of standard datasets. We show the impact of often neglected parameters of the face extraction stage on detection accuracy. We also document that existing PRNU-based methods cannot compete with state of the art methods based on deep learning but may be used to complement those in hybrid detection schemes.

Generation of synthetic biometric samples such as, for instance, fingerprint images gains more and more importance especially in view of recent cross-border regulations on security of private data. The reason is that biometric data is designated in recent regulations such as the EU GDPR as a special category of private data, making sharing datasets of biometric samples hardly possible even for research purposes. The usage of fingerprint images in forensic research faces the same challenge. The replacement of real datasets by synthetic datasets is the most advantageous straightforward solution which bears, however, the risk of generating "unrealistic" samples or "unrealistic distributions" of samples which may visually appear realistic. Despite numerous efforts to generate high-quality fingerprints, there is still no common agreement on how to define "high-quality'' and how to validate that generated samples are realistic enough. Here, we propose general requirements on synthetic biometric samples (that are also applicable for fingerprint images used in forensic application scenarios) together with formal metrics to validate whether the requirements are fulfilled. Validation of our proposed requirements enables establishing the quality of a generative model (informed evaluation) or even the quality of a dataset of generated samples (blind evaluation). Moreover, we demonstrate in an example how our proposed evaluation concept can be applied to a comparison of real and synthetic datasets aiming at revealing if the synthetic samples exhibit significantly different properties as compared to real ones.

The proof of origin of logs is becoming increasingly important. In the context of Industry 4.0 and to prevent illegal logging there is an increased interest to track each individual log. In the near future more and more sawmills will be equipped with a computed tomography (CT) scanner. In order to establish wood log traceability from the forest to the sawmill this work investigates log recognition based on RGB log end images captured in the forest and CT log images captured in the sawmill. The advantage of that approach is that CT scanners are already applied in big saw mills to optimize the saw cut and so the logs only have to be recorded once more in the forest which saves time and cost. To bridge the domain shift between CT and RGB images, we apply widely known domain adaption approaches and present a novel filtering approach. Log recognition is done using a convolutional neural network (CNN) based method using the triplet loss for CNN training and a novel shape descriptor. The results (equal error rate of 13%) show that the recognition of logs using different imaging modalities (RGB and CT) is indeed feasible, despite the challenging experimental setup.

The assessment of very low quality visual data is known to be difficult. In particular, the ability of humans to recognize encrypted visual data is currently impossible to determine computationally. The human vision research community has widely studied some particular topics, such as image quality assessment or the determination of a visibility threshold, while others are still barely researched, specifically visual content recognition. To this day, there does not exist a reliable recognition index that can be employed for such tasks. In order to enable the study of human image content recognition, and in an attempt to propose a corresponding recognizability index, we build a dataset of selectively encrypted images together with subjective ground-truth about their human intelligibility. The methods of acquisition, setup, protocol, outlier detection, are described and we suggest how to calculate a recognition score as well as a recognition threshold. The performance of traditional visual quality indices to predict human visual content recognition is assessed on these data and found to be inapt to estimate recognition of visual content. Contrasting, structure based recognition indices as proposed for this task are shown to represent a promising starting point for further research. To facilitate the creation of a recognition index and to foster further research into human visual content recognition and its relation to the human visual system we will make the database publicly available.

By bringing concepts of precision farming to intensive aquaculture fish production, it can be optimized to be more sustainable while focusing on fish welfare criteria. This requires a shift from mass to smart production and to consider each fish as an individual. Therefore, it is required to be able to identify each fish in a tank or sea cage. In this article, we prove the feasibility of fish identification using the iris as a biometric characteristic. Based on a new dataset, captured in a controlled out of water environment: 1) a fully automated iris recognition system is presented and utilized for the experiments and 2) the distinctiveness and the stability of the iris pattern of Atlantic salmon (Salmo salar) is assessed. Results prove the distinctiveness, which indicates that the iris pattern of Atlantic salmon is suited for biometric identification. However, the iris pattern has a low stability, which means it changes over time. Due to frequent interaction of fish and system, usually multiple times a day during feeding, there is ample opportunity to keep the biometric template up-to-date, which makes the lack of long-term stability a nonissue. It can be concluded that a biometric fish identification system is feasible, with the precondition that biometric templates of each fish are periodically updated to combat the low stability.

In this work, we investigate the possibility of generating a grayscale image of the finger vein from its binary template. This exercise would allow us to determine the invertibility of finger vein templates, and this has implications in biometric security and privacy. While such an analysis has been undertaken in the context of face, fingerprint and iris templates, this is the first work involving the finger vein biometric trait. The transformation from binary features to a grayscale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 7 distinct finger vein datasets. Results indicate that (a) it is possible to reconstruct finger vein images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization).

We conduct an ISO/IEC Standards 24745 and 30136 compliant assessment of block-based warping sample transformation techniques aiming for template protection. Particular focus is laid on the results' evaluation considering the evolution of face recognition technology ranging from more “historic” hand-crafted features to state-of-the-art deep-learning (DL) based schemes. It turns out that the high robustness of todays face recognition technology can handle geometrical distortions introduced by warping as another form of variability like pose, illumination, and expression variations, thereby disabling the intended protection functionality of warping. Therefore, block-based warping sample transformation must not be used as template protection technique for todays state-of-the-art face recognition schemes, while some settings could be identified providing template protection to some extent for less recent face recognition technology.

Finger vein recognition deals with the identification of subjects based on their venous pattern within the fingers. The recognition accuracy of finger vein recognition systems suffers from different internal and external factors. One of the major problems are misplacements of the finger during acquisition. In particular longitudinal finger rotation poses a severe problem for such recognition systems. The detection and correction of such rotations is a difficult task as typically finger vein scanners acquire only a single image from the vein pattern. Therefore, important information such as the shape of the finger or the depth of the veins within the finger, which are needed for the rotation detection, are not available. This work presents a CNN based rotation detector that is capable of estimating the rotational difference between vein images of the same finger without providing any additional information. The experiments executed not only show that the method delivers highly accurate results, but it also generalizes so that the trained CNN can also be applied on data sets which have not been included during the training of the CNN. Correcting the rotation difference between images using the CNN's rotation prediction leads to EER improvements between 50-260% for a well-established vein-pattern based method (Maximum Curvature) on four public finger vein databases.

Forensic analysis is used to detect image forgeries e.g. the copy move forgery and the object removal forgery, respectively. Counter forensic techniques (aka anti-forensic methods to fool the forensic analyst by concealing traces of manipulation) have become popular in the game of cat and mouse between the analyst and the attacker. Classical anti-forensic techniques targeting on SIFT keypoints have been established with particular emphasis on keypoint removal in the context of copy move forgery detection. In this paper we propose a forensic approach countering SIFT keypoint removal by changing to a different type of keypoints in forensic analysis, clearly demonstrating benefits over traditional SIFT keypoint oriented techniques.

We study the impact of highly correlated image content on the estimated photo response non-uniformity (PRNU) of a sensor unit and its impact on the sensor identification performance. Based on eight publicly available finger vein datasets, we show formally and experimentally that the nature of finger vein imagery can cause the estimated PRNU to be biased by image content and lead to a fairly bad PRNU estimate. Such bias can cause a false increase in sensor identification performance depending on the dataset composition. Our results indicate that independent of the biometric modality, examining the quality of the estimated PRNU is essential before the sensor identification performance can be claimed to be good.

Finger vein recognition deals with the identification of subjects based on their venous pattern within the fingers. There is a lot of prior work using hand crafted features, but only little work using CNN based recognition systems. This article proposes a new approach using CNNs that utilizes the triplet loss function together with hard triplet online selection for finger vein recognition. The CNNs are used for three different use cases: (1) the classical recognition use case, where every finger of a subject is considered as a separate class, (2) an evaluation of the similarity of left and right hand fingers from the same subject and (3) an evaluation of the similarity of different fingers of the same subject. The results show that the proposed nets achieve superior results compared to prior work on finger vein recognition using the triplet loss function. Furtherly, we show that different fingers of the same subject, especially symmetric fingers (same finger type but from different hand), show enough similarities to perform recognition. The last statement contradicts the current understanding in the literature for finger vein biometry, in which it is assumed that different fingers of the same subject are unique identities.

Most automated fingerprint recognition systems use minutiae points for comparing fingerprints. In the parlance of Computer Vision, minutiae can be viewed as handcrafted features, i.e., features that have been proposed by human experts for the task of fingerprint recognition. In this work, we raise the following question: Can a machine learning system automatically determine the significance of minutiae points for fingerprint matching? To this effect, a patch-based Siamese Convolutional Neural Network (CNN), which does not explicitly rely on the extraction of minutiae points, is designed and trained from scratch. The purpose of this network is to learn the most effective features for matching fingerprint images. The features learned by this network are analyzed using Gradient-weighted Class Activation Mapping (Grad-CAM) to determine if they correlate with the locations of minutiae points. Our experiments suggest that the proposed network automatically learns to focus on minutiae points, when available, for fingerprint matching. Thus, an automated learner without any explicit domain knowledge establishes the significance of minutiae points for fingerprint matching.

Biometric system security requires cryptographic protection of sample data under certain circumstances. We assess low complexity selective encryption schemes applied to JPEG2000 compressed iris data by conducting iris recognition on the selectively encrypted data. This paper specifically compares the effects of a recently proposed approach, i.e. applying selective encryption to normalised texture data, to encrypting classical sample data. We assess achieved protection level as well as computational cost of the considered schemes, and particularly highlight the role of segmentation in obtaining surprising results.

Electroencephalographic (EEG) biometric features have attracted considerable interest, but they have also drawn major criticism as having low stability. Moreover, most published studies ignore the potential effect of individual factors interacting with stability on the performance of the examined system. We examined the effects of age, sex, and neurological conditions in 60 subjects: 1) using a single EEG recording; 2) when pooling two EEG sessions; and 3) when performing cross-session cross-validation in a biometric system based on multivariate autoregressive measures, in order to extend previous work on autoregressive coefficients. Feature-level fusion and wrapped feature subset-selection in multivariate autoregressive coefficients (MVAR), power spectral density, and 14 additional measures derived from the MVAR model resulted in a maximum area under the curve of receiver operating characteristics (AUC of ROC)/top equal error rate of 98.85/5.34 for a single EEG, 95.51/9.70 for EEG-pooling, and 85.34/22 for cross-session cross-validation. This best result was obtained by the transfer function polynomial, which outperformed the MVAR coefficients, based on the example data set used in this paper. Age, sex, and pathology significantly interacted with the stability of features (p <; .001). We suggest further investigation of frequency-dependent measures derived from the MVAR model. We emphasize the serious problem of ignoring stability in most previously published research and recommend accurate reporting of individual factors when studying EEG biometric features in multiple sessions for enrolment and authentication on separate days.

Spherical Harmonic coefficients computed from human and automated hippocampus segmentations are used to discriminate patients suffering from MCI, TLE, and healthy individuals. In particular, we consider unilateral TLE and investigate which of the two hippocampi should preferably be used for reliable classifications. We find that the preferable classification setup strongly depends on the actual discrimination task. Qualification of human raters turns out to be crucial for discriminating patients with left lateralised TLE (TLE-L) from healthy controls and MCI patients (while automated segmentation techniques entirely fail in these two tasks). For discriminating TLE-R patients from healthy controls and TLE-L, automated segmentation tools, especially when fusing several ones, turn out to be a good choice. With respect to lateralisation, some results are well in accordance with expectations derived from studies using different approaches (e.g. better MCI and TLE-L discrimination obtained on the left hippocampus). Results deviating from those expectations may be explained by compensation effects as observed in case one hemisphere takes over responsibilities from the pathological one, thereby also affecting its own structure.

From literature it is known that full-reference visual quality indices are a poor fit for the estimation of visual security for selective encryption. The question remains whether no-reference visual quality indices can perform where full reference indices falter. Furthermore, no-reference visual quality indices frequently use machine learning to train a model of natural scene statistics. It would be of interest to be able to gauge the impact of learning statistics from selectively encrypted images on performance as quality estimators for encryption. In the following we will answer these two questions.

The electroencephalogram (EEG) was introduced as a method for the generation of biometric templates. So far, most research focused on the optimisation of the enrolment and authentication, and it was claimed that the EEG has many advantages. However, it was never assessed whether the biometric templates obtained from the EEG contain sensitive information about the enrolled users. In this work we ask whether we can infer personal characteristics such as age, sex, or informations about neurological disorders from these templates.

Shortly, within the member states of the European Union a serialization-based anti-counterfeiting system for pharmaceutical products will be introduced. This system requires a third party enabling to track serialized and enrolled instances of each product from the manufacturer to the consumer. An alternative to serialization is authentication of a product by classifying it as being real or fake using intrinsic or extrinsic features of the product. Thereby, one approach is packaging material classification using images of the packaging textures. While the basic feasibility has been proven recently, it is not clear if such an authentication system works with images captured with mobile devices. Thus, in this work mobile drug packaging authentication is investigated. The experimental evaluation provides results on single- and cross-sensor scenarios. Results indicate the principal feasibility and acknowledge open issues for a mobile device drug packaging authentication system.

Over the past years, an enormous variety of different chaos-based image and video encryption algorithms have been proposed and published. While any algorithm published undergoes some more or less strict experimental security analysis, many of those schemes are being broken in subsequent publications. In this paper, we show that two main motivations for preferring chaos-based image encryption over classical strong cryptographic encryption, namely computational effort and security benefits, are highly questionable. We demonstrate that several statistical tests, commonly used to assess the security of chaos-based encryption schemes, are insufficient metrics for security analysis. We do this experimentally by constructing obviously insecure encryption schemes and demonstrating that they perform well and/or pass several of these tests. In conclusion, these tests can only give a necessary, but by no means a sufficient condition for security. As a consequence of this paper, several security analyses in related work are questionable; further, methodologies for the security assessment for chaos based encryption schemes need to be entirely reconsidered.

The necessity of biometric template alignment imposes a significant computational load and increases the probability of false positive occurrences in biometric systems. While for some modalities, automatic pre-alignment of biometric samples is utilised, this topic has not yet been explored for systems based on the iris. This paper presents a method for pre-alignment of iris images based on the positions ofautomatically detected eye corners. Existing work in the area of automatic eye corner detection has hitherto only involved visible wavelength images; for the near-infrared images, used in the vast majority of current iris recognition systems, this task is significantly more challenging and as of yet unexplored. A comparative study of two methods for solving this problem is presented in this paper. The eye corners detected by the two methods are then used for the pre-alignment and biometric performance evaluation experiments. The system utilising image pre-alignment is benchmarked against a baseline iris recognition system on the iris subset of the BioSecure database. In the benchmark, the workload associated with alignment compensation is significantly reduced, while the biometric performance remains unchanged or even improves slightly.

Being aware of the origin (source sensor) of an iris images offers several advantages. Identifying the specific sensor unit supports ensuring the integrity and authenticity of iris images and thus detecting insertion attacks at a biometric system. Moreover, by knowing the sensor model selective processing, such as image enhancements, becomes feasible. In order to determine the origin (i.e. dataset) of near-infrared (NIR) and visible spectrum iris/ocular images, we evaluate the performance of three different approaches, a photo response non-uniformity (PRNU) based and an image texture feature based one, and the fusion of both. Our first set of experiments includes 19 different datasets comprising different sensors and image resolutions. The second set includes 6 different camera models with 5 instances each. We evaluate the applicability of the three approaches in these test scenarios from a forensic and non-forensic perspective.

In this paper we evaluate the effects of various data augmentation techniques on the automated classification of celiac disease using endoscopic imagery in the circumstances of limited training data. The used data augmentation techniques range from standard augmentation techniques like cropping patches and flipping to augmentation techniques using the full spectrum of affine or even projective transformations. We also present a novel technique that adjusts the lighting conditions depending on the scale changes caused by the augmentation. These augmentation techniques aim to generate augmented images that model the mucosa shown in the original image when conditions like the rotation of the endoscope or its viewpoint and distance to the mu-cosal wall changes. Tests are carried out using 5 different image representations including two convolutional neural networks and three shallow image representations. Our experiments showed that CNN's clearly benefit from augmentation techniques using affine and projective transformation, especially when the lighting conditions are adjusted.

Inferring topological and geometrical information from data can offer an alternative perspective in machine learning problems. Methods from topological data analysis, e.g., persistent homology, enable us to obtain such information, typically in the form of summary representations of topological features. However, such topological signatures often come with an unusual structure (e.g., multisets of intervals) that is highly impractical for most machine learning techniques. While many strategies have been proposed to map these topological signatures into machine learning compatible representations, they suffer from being agnostic to the target learning task. In contrast, we propose a technique that enables us to input topological signatures to deep neural networks and learn a task-optimal representation during training. Our approach is realized as a novel input layer with favorable theoretical properties. Classification experiments on 2D object shapes and social network graphs demonstrate the versatility of the approach and, in case of the latter, we even outperform the state-of-the-art by a large margin.

In automated hippocampus segmentation, issues related to ground truth rater variability, subject variability and variability of software segmentation accuracy are investigated in the context of 3 publicly available, out-of-the-box software packages. Ground truth variability among three manual raters is controlled using a majority voting based label fusion scheme and observed subject variability underpins the importance of availability of large scale ground truth.

Texture patch classification is an important task in many different computer-aided medical systems. Convolutional Neural Networks (CNN's) have become state-of-the-art for many computer vision tasks in recent years. In this paper, we propose the use of CNN's for the automated classification of colonic mucosa for colon polyp staging in the context of colon cancer screening. This deep learning approach has the property of extracting features and classifying images in the same architecture by exploiting directly the input image pixels being successful in handling distortions such as different light conditions, presence of partial occlusions, etc. For this type of deep learning approach it is common to require that the database contains large amounts of data, which is quite rare in the medical field. The method proposed allows the use of small patches (subimages) to increase the size of the database as well to classify different regions in the same image. We show experimentally that this model is more efficient than some of the commonly used features for colonic polyp classification.

This work proposes a filter bank based texture analysis method and applies it for the classification of colonic polyps. The filter masks of the filter bank are especially designed to enable a distinction between different types of polyps. The filter bank consists of four types of filters, where each filter is based on the Gaussian distribution respectively derivatives of the Gaussian distribution. Three of the four types of filters are directional sensitive. To achieve rotation invariance, only the maximum and minimum filter responses are accounted over differently oriented version of the directional filters. The final feature vector consists of the histograms over the filter responses. The method is tested on eight HD-endoscopic image databases. Five state-of-the-art approaches are additionally applied to the databases to compare their results with those of our filter bank approach. Experiments show that our proposed method clearly outperforms the state-of-the art approaches.

In this paper we present a metric for visual security evaluation of encrypted images, also known as visual security metric. Such a metric should be able to assess whether an image encryption method is secure or not. In order to consider intelligibility of objects in encrypted images our metric is based on image segmentation and applying a measure designed to evaluate the segmentation result. The visual security metrics' performance is evaluated using a selective encryption approach and compared to some general image quality metrics like PSNR, metrics suggested for encrypted images like Irregular Deviation and two metrics specifically designed for visual security evaluation. Our visual security metric performs better than all of the other tested metrics on the dataset and encryption algorithm we used during our experiments in terms of different correlation measures.

Iris localisation and segmentation are challenging and critical tasks in iris biometric recognition. Especially in non-cooperative and less ideal environments, their impact on overall system performance has been identified as a major issue. In order to avoid a propagation of system errors along the processing chain, this paper investigates iris fusion at segmentation-level prior to feature extraction and presents a framework for this task. A novel intelligent reference method for iris segmentation-level fusion is presented, which uses a learning-based approach predicting ground truth segmentation performance from quality indicators and model-based fusion to create combined boundaries. The new technique is analysed with regard to its capability to combine segmentation results (pupillary and limbic boundaries) of multiple segmentation algorithms. Results are validated on pairwise combinations of four open source iris segmentation algorithms with regard to the public CASIA and IITD iris databases illustrating the high versatility of the proposed method.

In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state-of-the-art methods on most of the databases. We will also show that the Weibull distribution is better suited to model the subband coefficient distribution than other commonly used probability distributions like the Gaussian distribution and the generalized Gaussian distribution. So this work gives a reasonable summary of wavelet based methods for colonic polyp classification and the huge amount of endoscopic polyp databases used for our experiments assures a high significance of the achieved results.

Tree log end biometrics is an approach to track logs from forest to further processing companies by means of log end images. The aim of this work is to investigate how to deal with the unrestricted rotational range of cross sections in log end images. Thus, the applicability of three different rotational pre-alignment strategies in the registration procedure is assessed. Template computation and matching is based on fingerprint and iris recognition techniques which were adopted and extended to work with log end images. To address these questions, a testset built up on 279 tree logs is utilized in the experiments. The evaluation assesses the basic performance of the rotational pre-alignment strategies and their impact on the verification and identification performances for different fingerprint- and iris-based configurations. Results indicate that rotational pre-alignment in the registration procedure is the main component to deal with rotation in log end biometrics. The best configurations achieve identification rates $${>}93\,\%$$ . By showing that cross sections in log end images can be rotated to a distinctive position, this work is a first step towards real word log end biometrics.

Visual security metrics are deterministic measures with the (claimed) ability to assess whether an encryption method for visual data does achieve its defined goal. These metrics are usually developed together with a particular encryption method in order to provide an evaluation of said method based on its visual output. However, visual security metrics themselves are rarely evaluated and the claim to perform as a visual security metric is not tied to the specific encryption method for which they were developed. In this paper, we introduce a methodology for assessing the performance of security metrics based on common media encryption scenarios. We systematically evaluate visual security metrics proposed in the literature, along with conventional image metrics which are frequently used for the same task. We show that they are generally not suitable to perform their claimed task.

In the fields of texture classification, the sizes of images significantly vary according to the respective classification scenario. Whereas quite small image patches mostly lead to good classification accuracies, increasing the image size sometimes even has a negative effect. In this work, we focus on derivatives of Local Binary Patterns as these feature extraction methods offer a high discriminative power and efficiency on the one hand an can be effectively analyzed on the other hand. The aim is to get new insight and furthermore to explore strategies which can help to increase the classification performance. We investigate these strategies which exploit the obviously high distinctiveness of small image patches and simultaneously the redundancy available in large image patches. Finally it can be concluded that the traditionally applied strategies for texture classification should be reconsidered in case of sufficiently large image data.

We show that the selective encryption of AC coefficient signs in DCT-based video formats is not suitable for use cases which require confidentiality, but can be used for some other application scenarios. By proposing a new assessment based on the method by Wu et al. we analyze the range of formats from the older JPEG standard to the more recent H.265/HEVC standard. We conclude that commonly used measures like exemplary PSNR values or key space calculations for theoretical brute force attacks are not sufficient to draw conclusions on the security of selective AC coefficient sign encryption.

Recent investigations on biometric log recognition using end face images indicated that shape information is beneficial for the biometric system performance. This study assesses the discriminative power and reliability of geometric features which are computed by means of segmented cross-sections and their pith positions. The experimental evaluation is based on cross-section images from 150 different logs, for which the ground truth of the boundary and pith position is known. By assessing the verification performance for ground truth data and automated segmentation/ pith estimation procedures this work highlights the basic discriminative power of geometric log end features and further validates their reliability in case of using automated procedures.

A large variety of well-known scale-invariant texture recognition methods is tested with respect to their scale invariance. The scale invariance of these methods is estimated by comparing the results of two test setups. In the first test setup, the images of the training and evaluation set are acquired under same scale conditions and in the second test setup, the images in the evaluation set are gathered under different scale conditions than those of the training set. For the first test setup, scale invariance is not needed, whereas for the second test setup, scale invariance is obviously crucial. The difference between the results of these two test setups indicates the scale invariance of a method (the higher the scale invariance the lower the difference). The scale invariance of the methods is additionally estimated by analyzing the similarity of the feature vectors of images and their scaled versions. Additionally to the scale invariance, we also test eventual viewpoint and illumination invariance of the methods. As texture databases for our tests we use the KTH-TIPS database and the CUReT database. Results imply that many of the considered methods are not as scale-invariant as expected.

Local Binary Patterns (LBPs) have been used in a wide range of texture classification scenarios and have proven to provide a highly discriminative feature representation. A major limitation of LBP is its sensitivity to affine transformations. In this work, we present a scale- and rotation-invariant computation of LBP. Rotation-invariance is achieved by explicit alignment of features at the extraction level, using a robust estimate of global orientation. Scale-adapted features are computed in reference to the estimated scale of an image, based on the distribution of scale normalized Laplacian responses in a scale-space representation. Intrinsic-scale-adaption is performed to compute features, independent of the intrinsic texture scale, leading to a significantly increased discriminative power for a large amount of texture classes. In a final step, the rotation- and scale-invariant features are combined in a multi-resolution representation, which improves the classification accuracy in texture classification scenarios with scaling and rotation significantly.

Local Binary Patterns and its derivatives have been widely used in the field of texture recognition over the last decade. A restriction of methods based on LBP is the variance in terms of signal scaling. This is mainly caused by the fixed LBP radius and the fixed support area of sampling points. In this work we present a general framework to enhance the scale-invariance of all LBP flavored methods, which can be applied to existing methods with minimal effort. Based on scale-normalized Laplacian of Gaussian extrema in scale-space, the global scale of a texture in question is estimated, combined with a confidence measure, to compute scale adapted patterns. By using the notion of intrinsic scales, textures are analyzed at appropriate LBP scales. A comprehensive experimental study shows that the scale-invariance of three different LBP based methods (LBP, LTP, Fuzzy LBP) is highly improved by the proposed extension.

We propose a selective encryption scheme for HEVC which allows for transparent encryption in a wide range of quantization parameters. Our approach focusses on the AC coefficient signs, since they can be altered directly in the bit stream without entropy reencoding. This allows for fast encryption and decryption while retaining full format-compliance and length-preservation. Furthermore, we show our approach's applicability for a number of use cases by evaluating the quality degradation and robustness against attacks.

Similar to the impact of ageing on human beings, digital image sensors develop ageing effects over time. Since these imager's ageing effects (commonly denoted as pixel defects) leave marks in the captured images, it is not clear whether this affects the accuracy of iris recognition systems. This paper proposes a method to investigate the influence of sensor ageing on iris recognition by simulative ageing of an iris test database. A pixel model is introduced and an ageing algorithm is discussed to create the test database. To establish practical relevance, the simulation parameters are estimated from the observed ageing effects of a real iris scanner over the timespan of 4 years.

The International Organization for Standardization (ISO) specifies iris biometric data to be recorded and stored in (raw) image form (ISO/IEC 19794-6), rather than in extracted templates, i.e. iris-codes. Existing literature confirms the applicability of lossy image compression in iris biometric systems, however, so far investigations on the impact of image compression on iris segmentation algorithms have remained elusive. In this work we examine the impact of severe image compression algorithms in particular, JPEG, JPEG 2000, and JPEG-XR, on the performance of different iris segmentation approaches. Experiments are carried out on an uncompressed iris database and, based on a manually annotated ground truth, effects of image compression on iris segmentation are quantified. It is found that surprisingly, JPEG causes the least segmentation errors over a wide range of high to medium bitrates (down to 0.3 bpp) despite of its weak performance in terms of PSNR rate-distortion behaviour.

Image degradations such as noise, blur and scale-variations are known to significantly affect the classification process of textured images. However, due to difficult visual according conditions, such degradation are often prevalent in digital real-world images. We show that these degradations not necessarily strongly affect the discriminative powers of features, in a scenario where similarly degraded images are classified. Contrarily, if the training and the evaluation set contain differently degraded images, the accuracies are decreasing extremely. In this paper, we exploit this knowledge and propose an approach which divides one large database into several smaller ones, each containing similarly degraded images. In order to get sensible database divisions, we use criteria adapted to the respective degradation. In experiments with several degradations, classifiers and feature extraction methods, we show that our method continuously and significantly enhances the classification accuracies.

We evaluate a set of general purpose bivariate image quality measures with respect to their ability to assess fingerprint-image quality. Our evaluation approach is able to identify specific weaknesses or strengths of quality indices with respect to certain types of quality impairments. To systematically generate corresponding test data, we apply StirMark image manipulations (as being developed in the context of watermarking robustness assessment) which are able to simulate a wide class of acquisition conditions and qualities, applicable to any given dataset and also of potential interest in forensic analysis. Experimental results document the competitiveness of the general purpose image quality measures in a comparison with dedicated univariate fingerprint-image quality measures.

In this work two practical issues of biometric log recognition using log end images are investigated: Temporal and longitudinal variances of log cross-sections (CSs). These variances are related to the requirement of robustness for biometric characteristics. A texture feature-based fingerprint matching technique is used to compute matching scores between CS images. Our experimental evaluation is based on the temporal and longitudinal variances of 35 slices of a single tree log which where captured at four time delayed sessions. Results indicate, that biometric log recognition using log end images is robust and is able to overcome both issues. This work contributes to the development of a biometric log recognition system by showing that a texture feature-based matching technique is applicable to log CSs.

This work proposes a new method for computing the local fractal dimension for the classification of colonic polyps. First an image is segmented by an algorithm based on the idea of the watershed transform. The resultant connected components (blobs) show the local mucosal structure at local minima and maxima in the image and model the pit pattern structure of the mucosa. The local fractal dimension is computed using two different filter masks, an anisotropic Gaussian filter mask and an elliptic binary filter mask, which are especially adapted to the shapes and sizes of the blobs. By specifically fitting shapes and sizes of the filter masks for each blob, our feature is scale, orientation and viewpoint invariant. The proposed method outperforms other methods commonly used for mucosal texture classification.

Authentication based on touch-less mid-air gestures would benefit a multitude of ubicomp applications, which are used in clean environments (e.g., medical environments or clean rooms). In order to explore the potential of mid-air gestures for novel authentication approaches, we performed a series of studies and design experiments. First, we collected data from more then 200 users during a three-day science event organised within a shopping mall. This data was used to investigate capabilities of the Leap Motion sensor and to formulate an initial design problem. The design problem, as well as the design of mid-air gestures for authentication purposes, were iterated in subsequent design activities. In a final study with 13 participants, we evaluated two mid-air gestures for authentication purposes in different situations, including different body positions. Our results highlight a need for different mid-air gestures for differing situations and carefully chosen constraints for mid-air gestures.

Scale invariant texture analysis is a fundamental challenge in image processing. As a consequence of the scale invariance, these kind of features are often characterized by a lower discriminative power. We observed, that scale invariant features did not pose a benefit in classification scenarios with varying scales in the training set. This is supposed to be an effect caused by an implicit scale selection done by the classification method. In this work, we analyze this effect based on the k-nearest neighbor classifier. Inspired by this effect, we employ global scale estimation algorithm utilizing scale-normalized Laplacian of Gaussian extrema in scale space, to improve the classification accuracies of scale variant features in a scenario with varying scales. We propose a general framework for scale-adaptive classification, which proved to improve the classification accuracies with a variety of feature extraction methods in such a scenario.

In this work we investigate whether cross endoscopic modality classification of colonic polyps is feasible, i.e. images of a high-magnification endoscope are used as training set to classify images of a high-definition endoscope. In order to compensate the scale differences between the images acquired with the different imaging modalities we apply different super-resolution methods to endoscopic high-definition sequences. We then use a set of feature extraction methods for the classification of the super-resolution reconstruction results. To be able to assess whether super-resolution algorithms are helpful in this scenario, we also compare the results obtained from these experiments against the classification results based on original high-definition frames and against classification rates based on upscaled versions of high-definition frames. We show that classifying images acquired with a high-definition endoscope and training the underlying classifier with images acquired with a high-magnification endoscope is feasible, but the improvements by using super-resolution algorithms are highly feature-dependent.

Methods based on Local Binary Patterns have been used successfully in a wide range of texture classification tasks. A restriction shared by all methods based on Local Binary Patterns is the high sensitivity to signal scale. In recent work we presented a general framework for scale-adaptive computation of Local Binary Patterns, improving the accuracy in texture classification scenarios involving varying texture-scales highly. In this work, the scale-adaptive methodology is extended by an orientation-adaptive computation of patterns, leading to a scale- and rotation invariant classification. The results suggest that estimating a global orientation to build orientation-adaptive LBPs is superior to the previously introduced rotation-invariant encodings. The proposed framework allows the use of the highly-discriminative LBPs in less-constrained situations, where both orientation, as well as scale variations, are to be expected.

Classical iris biometric systems assume ideal environmental conditions and cooperative users for image acquisition. When conditions are less ideal or users are uncooperative or unaware of their biometrics being taken the image acquisition quality suffers. This makes it harder for iris localization and segmentation algorithms to properly segment the acquired image into iris and non-iris parts. Segmentation is a critical part in iris recognition systems, since errors in this initial stage are propagated to subsequent processing stages. Therefore, the performance of iris segmentation algorithms is paramount to the performance of the overall system. In order to properly evaluate and develop iris segmentation algorithm, especially under difficult conditions like off angle and significant occlusions or bad lighting, it is beneficial to directly assess the segmentation algorithm. Currently, when evaluating the performance of iris segmentation algorithms this is mostly done by utilizing the recognition rate, and consequently the overall performance of the biometric system. In order to streamline the development and assessment of iris segmentation algorithms with the dependence on the whole biometric system we have generated a iris segmentation ground truth database. We will show a method for evaluating iris segmentation performance base on this ground truth database and give examples of how to identify problematic cases in order to further analyse the segmentation algorithms.

In this paper, we assess the adequacy of slice groups for the reduction of drift which occurs in bit-stream-based region of interest encryption in SVC. For practical surveillance camera applications, we introduce the concept of all-grey base layers which simplify the encryption of regions of interest while obeying all standard-imposed base layer restrictions. Furthermore, we show that the use of slice groups is possible with relatively low overhead for most practical configurations with two or three spatial layers. In addition, we analyze the effect of spatial resolution on overhead, showing that an increase in resolution decreases the relative overhead.

JPEG XR is the most recent still image coding standard, and custom security features for this format are required for fast adoption of the standard. Format-compliant encryption schemes are important for many application scenarios but need to be highly customised to a specific recent format like JPEG XR. This paper proposes, discusses, and evaluates a set of format-compliant encryption methods for the JPEG XR standard: coefficient scan order permutation, sign bit encryption, transform-based encryption, random level shift encryption, index-based VLC encryption, and encrypting entire frequency bands are considered. All algorithms are thoroughly evaluated by discussing possible compression impact, by assessing visual security and cryptographic security, and by discussing applicability in real-world scenarios. Most techniques are found to be insecure and, in a cryptographic sense, have a limited range of applicability and cannot be applied to JPEG XR bitstreams in an efficient manner. Encrypting entire frequency bands is identified to be a good solution in case a weaker form of format compliance can be accepted.

In this work we propose a novel non-blind H.264/CAVLC structure-preserving substitution watermarking algorithm. The proposed watermarking algorithm enables extremely efficient watermark embedding by simple bit substitutions (substitution watermarking). The bit-substitutions change the motion vector differences of non-reference frames. Furthermore our watermarking algorithm can be applied in applications scenarios which require that watermarking preserves the length of the bitstream units (structure-preserving watermarking). The watermark detection works in the image domain and thus is robust to video format changes. The quality and robustness of the approach are in depth evaluated and analyzed, the quality evaluation is backed up by subjective evaluations. Comparison to the state-of-the-art indicates a superior performance of our watermarking algorithm.

The main question we try to answer in this work is whether it is feasible to employ super-resolution (SR) algorithms to increase the spatial resolution of endoscopic high-definition (HD) images in order to reveal new details which may have got lost due to the limited endoscope magnification inherent to the HD endoscope used (e.g. mucosal structures). For this purpose we propose a SR algorithm, which is based on the Projection onto convex sets (POCS) approach. This algorithm is able to avoid over-sharpening, which is often seen with other methods. Since POCS-based approaches are iterative ones, we also propose an adaptive iteration scheme. We compare the quality of the reconstruction of our method against the quality achieved by other SR methods. This is done on standard test images as well as on images obtained from endoscopic video frames. We show that, while our approach produces competitive results on standard test images, we are not able to reveal new details in endoscopic images for various reasons.

Interlaced scanning is a technique that has been widely in use to double the perceived frame rate without increasing the used bandwidth. Interlaced scanning is still in use by endoscopic video hardware today. Towards the development of an automated decision support system we focus on the evaluation of the impact of de-interlacing techniques on the accuracy of automated classification of endoscopic video data with indication for celiac disease. In a large experimental setup a variety of de-interlacing methods are evaluated using a set of feature extraction methods from the fields of pattern recognition and medical image analysis.

We propose and evaluate different methods to signal position and size of encrypted RoIs (Regions of Interest) in JPEG images. After discussing various design choices regarding the encoding of RoI coordinates with a minimal amount of bits, we discuss both, existing and newly proposed approaches to signal the encoded coordinates inside JPEG images. By evaluating the different signalling methods on various data sets, we show that several of our proposed encoding methods outperform JBIG in this special use case. Furthermore, we show that one of our proposed signalling methods allows length-preserving lossless signalling, i.e., storing RoI coordinates in a format-compliant way inside the JPEG images without quality loss or change of file size.

A technique to generate non-invertible and revocable iris templates is proposed employing key-dependent wavelet transforms. In particular, parametrised wavelet-filters and wavelet packets are used in feature extraction in replacement of a pyramidal D4 wavelet transform. Since the template generation process is non-invertible by design, the overall scheme is non-invertible as well. Recognition accuracy is found to be high as long as personal tokens remain secret, templates can be revoked by simply exchanging the wavelet transform applied in the feature extraction process.

We propose to establish a standardised tool in fingerprint recognition robustness assessment, which is able to simulate a wide class of acquisition conditions, applicable to any given dataset and also of potential interest in forensic analysis. As an example, StirMark image manipulations (as being developed in the context of watermarking robustness assessment) are applied to fingerprint data to generate test data for robustness evaluations, thereby interpreting certain image manipulations as being highly related to realistic fingerprint acquisition conditions. Experimental results involving three different minutiae-based fingerprint matching schemes applied to FVC2004 data underline the need for standardised testing and a corresponding simulation toolset.

Scale invariant texture recognition methods are applied for the computer assisted diagnosis of celiac disease. In particular, emphasis is given to techniques enhancing the scale invariance of multi-scale and multi-orientation wavelet transforms and methods based on fractal analysis. After fine-tuning to specific properties of our celiac disease imagery database, which consists of endoscopic images of the duodenum, some scale invariant (and often even viewpoint invariant) methods provide classification results improving the current state of the art. However, not each of the investigated scale invariant methods is applicable successfully to our dataset. Therefore, the scale invariance of the employed approaches is explicitly assessed and it is found that many of the analyzed methods are not as scale invariant as they theoretically should be. Results imply that scale invariance is not a key-feature required for successful classification of our celiac disease dataset.

We evaluate different cross-validation (CV) protocols for an automated classification of colonic polyps. For this purpose we select six previously developed methods which achieved promising results already in the past. We then evaluate the methods using the cross-validation protocols leave-one-image-out (LOO-CV), leave-one-parent-image-out (LOPIO-CV), leave-one-lesion-out (LOLO-CV), and leave-one-patient-out (LOPO-CV). We show that, in general, the more restrictive cross-validation protocols lead to high results drops. While in case of LOO-CV the accuracies are rather high across all methods evaluated, the picture changes the more strictness a cross-validation mode imposes on the set of training images.

We have shown in previous work that problems inherent in the automated diagnosis of standard gastroscopic videos, such as distortion and noise handling, can be handled implicitly, to some extent, by using a one-class support vector machine (SVM) classifier. A video sequence of a standard endoscopic procedure is characterized by rapid changes of perspective towards an inspected area causing various shots at different distances as well as non-predictable transits through gastrointestinal regions. In this work we examine to what extent a one-class support vector machine combined with features based on local binary patterns (LBP) variants can be used to implicitly handle varying camera distances to the mucosa as well as the non-predictable topographical changes during endoscopy.

JPEG2000 Part 2 allows the application of arbitrary wavelet decomposition structures (wavelet packet bases). Efficient anisotropic wavelet packet basis selection for the coding framework of JPEG2000 has been developed and evaluated. Previous work focused on isotropic wavelet packet basis selection algorithms for JPEG2000, which serves as basis for the performance of the assessment of anisotropic wavelet basis selection. Several cost functions are applied in a top-down anisotropic wavelet packet selection scheme. Our evaluations employ state-of-the-art quality assessment tools supplementary to PSNR evaluations.

Key-dependent Wavelet Packet Transforms (KDWPT) have been proposed for image encryption and especially for the joint application with the JPEG2000 compression framework. An assumed advantage of this compression integrated encryption scheme is its assumed negligible computational demand. In this work we analyze the assumption (KDWPT are lightweight compared to conventional encryption) both practically by experiments with state-of-the-art implementations and theoretically (by developing a proper model for the complexity of KDWPT).

In wireless and mobile environments, universal media access is a key concept to facilitate efficient transmission of visual data by simple bitstream adaptation which relies on scalable coding schemes. Six different blind watermarking schemes are compared with respect to their watermark detection performance on scaled visual data. Specifically, resolution and quality scalability is considered in JPEG2000 and JPEG and the found watermark detection performance is related to the employed watermarking embedding strategies. It is found that in wireless environments, careful adjustment between watermark embedding and scalability modes is required to result in the desired watermark detection properties.

Distortion correction in two variants is applied to endoscopic duodenal imagery aiming at an improvement of automated classification of celiac disease affected mucosa patches. In a set of heterogeneous feature extraction techniques, only geometry and shape related ones are able to benefit from distortion correction, while for others, even a decrease of classification accuracy is observed. Different types of distortion correction do not lead to significantly different behaviour in the observed application scenario.

Privacy enhancing technologies in video surveillance are discussed for JPEG2000 codestreams. Protection accuracy, compression impact, and computational requirements are assessed and compared. Recommendations are given which approach is favourable under certain conditions.

Abstract We analyze and discuss encryption schemes for JPEG2000 based on the wavelet packet transform with a key-dependent subband structure. These schemes have been assumed to reduce the runtime complexity of encryption and compression. In addition to this "lightweight" nature, other advantages like encrypted domain signal processing have been reported. We systematically analyze encryption approaches based on key-dependent subband structures in terms of their impact on compression performance, their computational complexity and the level of security they provide as compared to more classical techniques. Furthermore, we analyze the prerequisites and settings in which the previously reported advantages actually hold and in which settings no advantages can be observed. As a final outcome it has to be stated that the compression integrated encryption approach based on the idea of secret wavelet packets can not be recommended.

In this work we propose a novel method to describe local texture properties within color images with the aim of automated classification of endoscopic images. In contrast to comparable Local Binary Patterns operator approaches, where the respective texture operator is almost always applied to each color channel separately, we construct a color vector field from an image. Based on this field the proposed operator computes the similarity between neighboring pixels. The resulting image descriptor is a compact 1D-histogram which we use for a classification using the k-nearest neighbors classifier. To show the usability of this operator we use it to classify magnification-endoscopic images according to the pit pattern classification scheme. Apart from that, we also show that compared to previously proposed operators we are not only able to get competitive classification results in our application scenario, but that the proposed operator is also able to outperform the other methods either in terms of speed, feature compactness, or both.

Universal Multimedia Access (UMA) calls for solutions where content is created once and subsequently adapted to given requirements. With regard to UMA and scalability, which is required often due to a wide variety of end clients, the best suited codecs are wavelet based (like the MC-EZBC) due to their inherent high number of scaling options. However, most transport technologies for delivering videos to end clients are targeted toward the H.264/AVC standard or, if scalability is required, the H.264/SVC. In this paper we will introduce a mapping of the MC-EZBC bitstream to existing H.264/SVC based streaming and scaling protocols. This enables the use of highly scalable wavelet based codecs on the one hand and the utilization of already existing network technologies without accruing high implementation costs on the other hand. Furthermore, we will evaluate different scaling options in order to choose the best option for given requirements. Additionally, we will evaluate different encryption options based on transport and bitstream encryption for use cases where digital rights management is required.

Video encryption has been heavily researched in the recent years. This survey summarizes the latest research results on video encryption with a special focus on applicability and on the most widely-deployed video format H.264 including its scalable extension SVC. The survey intends to give researchers and practitioners an analytic and critical overview of the state-of-the-art of video encryption narrowed down to its joint application with the H.264 standard suite and associated protocols (packaging/streaming) and processes (transcoding/watermarking).

This paper discusses optimal wavelet packet basis selection within JPEG2000. Algorithms for rate-distortion optimal wavelet packet basis selection in JPEG2000 are presented and compared to more efficient wavelet packet basis selection schemes. Both isotropic and anisotropic wavelet packet bases are considered. For the first time, computationally efficient heuristics are compared to the best bases in the standardized coding framework of JPEG2000. For the first time, the maximum performance gains of custom wavelet packets in JPEG2000 can be assessed. The algorithms are evaluated on a wide range of highly textured image data.

Fuzzy commitment schemes have been established as a reliable means of binding cryptographic keys to binary feature vectors extracted from diverse biometric modalities. In addition, attempts have been made to extend fuzzy commitment schemes to incorporate multiple biometric feature vectors. Within these schemes potential improvements through feature level fusion are commonly neglected. In this paper a feature level fusion technique for fuzzy commitment schemes is presented. The proposed reliability- balanced feature level fusion is designed to re-arrange and combine two binary biometric templates in a way that error correction capacities are exploited more effectively within a fuzzy commitment scheme yielding improvement with respect to key-retrieval rates. In experiments, which are carried out on iris-biometric data, reliability-balanced feature level fusion significantly outperforms conventional approaches to multi-biometric fuzzy commitment schemes confirming the soundness of the proposed technique.

To facilitate the embedding of fingerprinting payload corresponding to multiple re-sellings of a single image, multiple re-watermarking is a convenient technique. In this application context, oblivious detection techniques are important. Quantisation-based embedding technique are difficult to apply in this context without adapting them to the multiple watermarking scenario. We investigate two extensions to a wavelet coefficient-tree based embedding technique which turn out to improve detection performance in case of multiple re-watermarking.

Cancelable biometric systems are designed to provide intrinsic protection for biometric templates in case of a database breach. In this paper a security survey of a cancelable iris recognition system is conducted. It uses block permutation and remapping of the iris texture as a strategy for template protection. Two adjunctive scenarios and their impact on the security of the system are examined. First off it is assumed that an attacker got a hold on a single iris template. In the second scenario it is presumed that multiple templates of the same biometric characteristic are available to an attacker. The scenario of a so called ”Coalition Attack”. The test runs conducted suggest that the system does offer some resistance against a template theft but sacrificing overall system performance and usability for it.

In this paper, we introduce a Goodness-of-Fit test for the Multivariate Exponential Power (MEP) distribution, a multivariate extension of the Generalized Gaussian, which has recently gained considerable interest as a model for wavelet coefficients in the context of color image retrieval and spread-spectrum watermarking. We present a size and power study of this test and show Goodness-of-Fit results for wavelet coefficients of natural and texture images from various popular databases.

Efficient rate-distortion optimal wavelet packet basis selection for the coding framework of JPEG2000 has been developed and evaluated. JPEG2000 Part 2 allows the application of arbitrary decomposition structures (wavelet packet bases). However, only a subset of all possible wavelet packet bases is permitted in the JPEG2000 standard. As we have developed a rate-distortion optimal wavelet packet basis selection algorithm, we can determine the cost of these restrictions for the first time. A further focus is on the efficient implementation of wavelet packet basis selection. The best basis selection in a rate-distortion sense is computationally feasible, yet not inexpensive. Several more efficient wavelet packet selection algorithms have been developed and are compared to the actually best solution. Our evaluations employ state-of-the-art quality assessment tools supplementary to PSNR evaluations.

In this paper, we introduce a novel and cost effective approach to calibrate the geometric properties of a far-infrared (IR) sensor. We further demonstrate that fully automatic sensor-to-sensor calibration is feasible in a setup involving a laser range scanner, IR cameras as well as conventional cameras. The calibration result then serves as a basis for upsampling range measurements to the resolution of the IR or visible-light camera images. Since our approach allows to rely on IR information instead of visible-light information for upsampling, bad light conditions or even no visible light at all are no limitation. From a practical point of view, we only require one calibration board of relatively small size which facilitates application in outdoor environments and further allows seamless integration of the IR camera in an existing multi-sensor platform. Our experimental results demonstrate that IR images are particularly useful to obtain reasonable depth information for living objects, when visible-light cameras are either blind or require impractical exposure times. In fact, our approach provides a convenient solution to IR camera calibration and integration, an issue which is particularly important in scenarios where sensors are not permanently mounted on vehicles and consequently require on-site adjustment and calibration.

Form a privacy perspective most concerns against the common use of biometrics arise from the storage and misuse of biometric data. Biometric cryptosystems and cancelable biometrics represent emerging technologies of biometric template protection addressing these concerns and improving public confidence and acceptance of biometrics. In addition, biometric cryptosystems provide mechanisms for biometric-dependent key-release. In the last years a significant amount of approaches to both technologies have been published. A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented. State-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.

This article aims at lightweight, blind detection of additive spread-spectrum watermarks in the DWT domain. We focus on two host signal noise models and two types of hypothesis tests for watermark detection. As a crucial point of our work we take a closer look at the computational requirements of watermark detectors. This involves the computation of the detection response, parameter estimation and threshold selection. We show that by switching to approximate host signal parameter estimates or even fixed parameter settings we achieve a remarkable improvement in runtime performance without sacrificing detection performance. Our experimental results on a large number of images confirm the assumption that there is not necessarily a tradeoff between computation time and detection performance.

In this paper, we investigate a novel joint statistical model for subband coefficient magnitudes of the dual-tree complex wavelet transform, which is then coupled to a Bayesian framework for content-based image retrieval. The joint model allows to capture the association among transform coefficients of the same decomposition scale and different color channels. It further facilitates to incorporate recent research work on modeling marginal coefficient distributions. We demonstrate the applicability of the novel model in the context of color texture retrieval on four texture image databases and compare retrieval performance to a collection of state-of-the-art approaches in the field. Our experiments further include a thorough computational analysis of the main building blocks, runtime measurements, and an analysis of storage requirements. Eventually, we identify a model configuration with low storage requirements, competitive retrieval accuracy, and a runtime behavior, which enables the deployment even on large image databases.

We study the watermarking of 2D vector data and introduce a framework which preserves topological properties of the input. Our framework is based on so-called maximum perturbation regions (MPR) of the input vertices, which is a concept similar to the just-noticeable-difference constraint. The MPRs are computed by means of the Voronoi diagram of the input and allow us to avoid (self-)intersections of input objects that might result from the embedding of the watermark. We demonstrate and analyze the applicability of this new framework by coupling it with a well-known approach to watermarking that is based on Fourier descriptors. However, our framework is general enough such that any robust scheme for the watermarking of vector data can be applied.

This paper discusses optimal wavelet packet basis selection within JPEG2000. Two algorithms of Lagrangian rate distortion optimal wavelet packet basis selection for JPEG2000 are presented. The first and more conservative approach considers the JPEG2000 packet body data in the rate distortion optimization only, while the other technique additionally integrates packet header data. The algorithms are evaluated on the FVC2004 fingerprint databases and other textured image data. Results demonstrate that inclusion of header data information into rate distortion optimization leads to superior compression results. For the first time the maximum performance gains of custom isotropic wavelet packets in JPEG2000 can be assessed.

We present a system for an automated colon cancer detection based on the pit pattern classification. In contrast to previous work we exploit the visual nature of the underlying classification scheme by extracting features based on detected edges. To focus on the most discriminative subset of features we use a greedy forward feature subset selection. The classification is then carried out using the k-nearest neighbors (k-NN) classifier. The results obtained are very promising and show that an automated classification of the given imagery is feasible by using the proposed method.

In this paper we propose a modified hill-climbing attack to iris biometric systems. Applying our technique we are able to effectively gain access to iris biometric systems at very low effort. Furthermore, we demonstrate that reconstructing approximations of original iris images is highly non-trivial.

Performing identification on large-scale biometric databases requires an exhaustive linear search. Since biometric data does not have any natural sorting order, indexing databases, in order to minimize the response time of the system, represents a great challenge. In this work we propose a biometric hash generation technique for the purpose of biometric database indexing, applied to iris biometrics. Experimental results demonstrate that the presented approach highly accelerates biometric identification.

The impact of using different wavelet packet subband structures in JPEG2000 on the matching accuracy of a fingerprint recognition system is investigated. In particular, we relate rate-distortion performance as measured in PSNR to the matching scores as obtained by the recognition system. Employing wavelet packets instead of the dyadic wavelet transform turns out to be of advantage only in case of low bitrates (i.e. high compression rates). For such settings, the good performance of the WSQ structure is confirmed also in JPEG2000 and in particular we get better recognition accuracy using the WSQ structure as compared to the employment of a rate-distortion optimising subband selection approach

This paper contemplates the framework of probabilistic image retrieval in the wavelet domain from a computational point of view. We not only focus on achieving high retrieval rates, but also discuss possible performance bottlenecks which might prevent practical application. We propose a novel retrieval approach which is motivated by previous research work on modeling the marginal distributions of wavelet transform coefficients. The building blocks of our work are the dual-tree complex wavelet transform and a number of statistical models for the coefficient magnitudes. Image similarity measurement is accomplished by using closed-form solutions for the Kullback-Leibler divergences between the statistical models. We provide an in-depth computational analysis regarding the number of arithmetic operations required for similarity measurement and model parameter estimation. The experimental retrieval results on a widely used texture image database show that we achieve competitive retrieval results at low computational cost.

This paper describes an application of machine learning techniques and evolutionary algorithms to colon cancer diagnosis. We propose an automated classification system for endoscopical images, which is supposed to support physicians in making correct decisions. Classification is done according to the pit-pattern scheme, which defines two/six different classes based on the occurrence of patterns on the mucosa. All discriminative information for classification is obtained by filtering an image's frequency domain. A major part of this paper is devoted to the search for proper frequency filters. An extensive experimental study compares different search strategies and the resulting classification accuracies. We result in a top classification accuracy of 96.9% and 86.8% for the two- and six-classes case, respectively, using a database of 484 zoom-endoscopic images. We observe a tendency toward the employment of lower frequency filter structures for the best classification settings.

In this paper we present a novel watermark detector for additive spread-spectrum watermarking in the wavelet transform domain of color images. We propose to model the highly correlated DWT subbands of the RGB color channels by multivariate power-exponential distributions. This statistical model is then exploited to derive a likelihood ratio test for watermark detection. Our results indicate that joint statistical modeling of color DWT detail subbands leads to increased detection performance compared to previous approaches, namely watermarking of the luminance channel only, decorrelating the color bands, or relying on a joint Gaussian host signal model.

We present a Copula-based statistical model of complex wavelet coefficient magnitudes for color texture image retrieval. Our model is based on two-parameter Weibull distributions and a multivariate Student t Copula. For similarity measurement we employ a Monte- Carlo approach to approximate the Kullback-Leibler divergence between two models. The experimental retrieval results show that the incorporation of the dependency structure between subbands significantly improves retrieval accuracy compared to previous approaches.

In this paper we investigate the applicability of different parts of the JPEG2000 codestream for authentication. Apart from the packet body different classes of information contained in the packet header are investigated. We report on experimental results obtained with a large test set of natural images to assess how discriminative and how sensitive each class of information is. Specific attacks against authentication schemes, that use selective hashing of either packet bodies (as proposed in literature) or packet headers, are presented.

This letter describes an attack on the recently proposed ldquowatermarking method based on significant difference of wavelet coefficient quantizationrdquo by Lin While the method is shown to be robust against many signal processing operations, security of the watermarking scheme under intentional attack exploiting knowledge of the implementation has been neglected. We demonstrate a straightforward attack which retains the fidelity of the image. The method is therefore not suitable for copyright protection applications. Further, we propose a countermeasure which mitigates the shortcoming.

This paper presents a new image feature extraction approach in the wavelet domain. We incorporate color-channel information of the LAB color space into the feature extraction process by computing variances from decorrelated detail subbands of the stationary wavelet transform. We evaluate our approach on a medical image classification problem using a k-nearest neighbor classifier and sequential forward feature selection. our experimental results, which include a comparative study to the popular color wavelet energy correlation signatures show that we can produce highly discriminative feature sets in terms of leave-one-out classification accuracy.

In this work, we present a texture-image retrieval approach, which is based on the idea of measuring the Kullback-Leibler divergence between the marginal distributions of complex wavelet coefficient magnitudes. We employ Kingsbury's dual-tree complex wavelet transform for image decomposition and propose to model the detail subband coefficient magnitudes by either two-parameter Weibull or Gamma distributions for which we provide closed-form solutions to the Kullback-Leibler divergence. The experimental results indicate that our approach can achieve higher retrieval rates than the classical approach of using the pyramidal discrete wavelet transform together with the generalized Gaussian model for detail subband coefficients.

Wavelet packets are shown to be a possible means to limit watermark interference in multiple re-watermarking algorithms. It turns out that when demanding a certain distance among wavelet packet subband trees, a significant improvement of watermark detection correlation values may be achieved in such a scenario.

The temporal correlation between adjacent video frames poses a severe challenges for video watermarking applications. Motion-coherent watermarking has been recognized as a strategy to embed watermark information in video frames, resistant to collusion attacks. The motion-compensated temporal wavelet transform (MC-TWT) provides an efficient tool to separate static and dynamic components of a video scene and enables motion-coherent water-marking. In this paper, we extend a MC-TWT domain watermarking scheme with blind detection, i.e. motion estimation and watermark detection is performed without reference to the unwatermarked video. Our results show that motion-coherent watermarking can be combined with a blind detector, widening the applicability of MC-TWT domain watermarking beyond forensics (where the unwatermarked content is assumed to be available).

An encryption approach for H.264/AVC and SVC is proposed. Although the bitstream (format stream) is encrypted with state-of-the-art symmetric ciphers, H.264/AVC and SVC compliance is preserved. Standard compliant encoder/decoder and conventional symmetric ciphers, e.g., in specialized hardware, can still be employed – a significant advantage compared to previous work. The approach is suitable for a wide range of application scenarios.

Two lightweight encryption schemes for fingerprint images based on a bit-plane representation of the data are assessed. We demonstrate a low complexity attack against a scheme recently proposed in literature which exploits one of several weaknesses found. A second scheme is evaluated with respect to two fingerprint recognition systems and recommendations for its safe use are given.

This work presents a classification approach for images taken from magnifying colonoscopy. Classification is done according to the pit pattern scheme. Images are not classified directly in the proposed classifier. Instead, they are transformed to a frequency domain using discrete cosine or Fourier transformation. Feature selection is optimized using a genetic algorithm, the actual classification is done using standard methods from statistical pattern recognition (a Bayes normal classifier).

In this paper, we propose a set of new image features for the classification of zoom-endoscopy images. The feature extraction step is based on fitting a two-parameter Weibull distribution to the wavelet coefficient magnitudes of sub-bands obtained from a complex wavelet transform variant. We show, that the shape and scale parameter possess more discriminative power than the classic mean and standard deviation based features for complex subband coefficient magnitudes. Furthermore, we discuss why the commonly used Rayleigh distribution model is suboptimal in our case.

We analyze a recently published lightweight encryption scheme for fingerprint images and discuss several shortcomings. A low-cost attack on this scheme is proposed, which allows access to the full plaintext for most given ciphertexts. We give some recommendations for improvements of the encryption scheme, but conclude that the analyzed scheme remains insecure.

The impact of using different lossy compression algorithms on the matching accuracy of fingerprint and face recognition systems is investigated. In particular, we relate rate-distortion performance as measured in PSNR to the matching scores as obtained by the recognition systems. JPEG2000 and SPIHT are correctly predicted by PSNR to be the most suited compression algorithms to be used in fingerprint and face recognition systems. Fractal compression is identified to be least suited for the use in the investigated recognition systems, although PSNR suggests JPEG to deliver worse recognition results in the case of face imagery. JPEG compression performs surprisingly well at high bitrates in face recognition systems, given the low PSNR performance observed.

JPEG2000 encryption has become a widely discussed topic and quite a number of contributions have been made. However, little is known about JPEG2000 compression parameters and their influence on the security and performance of format, compliant encryption schemes. In this work, a thorough analysis of this topic is presented with a focus on format-compliant packet body encryption as sketched in the FCD 15444-8 (JPSEC). A proof for the reversibility of JPSEC format-compliant packet body encryption is given. As format-compliant packet body encryption preserves the JPEG2000 headers, which severely compromises the security, we additionally discuss packet header encryption with a special focus on format compliance and the influence of compression parameters on these schemes. Copyright © 2007 Dominik Engel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This paper analyzes the robustness properties of 2D chaotic map image encryption schemes. We investigate the behavior of such block ciphers under different channel error types and find the transmission error robustness to be highly dependent on the type of error occurring and to be very different as compared to the effects when using traditional block ciphers like AES. Additionally, chaotic-mixing-based encryption schemes are shown to be robust to lossy compression as long as the security requirements are not too high. This property facilitates the application of these ciphers in scenarios where lossy compression is applied to encrypted material, which is impossible in case traditional ciphers should be employed. If high security is required chaotic mixing loses its robustness to transmission errors and compression, still the lower computational demand may be an argument in favor of chaotic mixing as compared to traditional ciphers when visual data is to be encrypted.

A lightweight encryption scheme for JPEG 2000 based on the wavelet packet transform is proposed. This scheme significantly reduces the amount of data to be encrypted compared to full encryption and other partial or selective encryption schemes, at the cost of increased computational complexity in the compression pipeline. We investigate the applicability of this approach in two scenarios: for providing full confidentiality and for its utility as a transparent encryption scheme. We evaluate the presented scheme in the context of each scenario with respect to its impact on compression performance, its complexity, the level of security it provides, and its applicability

A lightweight encryption technique for JPEG2000 with optional support for transparent encryption is proposed. Randomized anisotropic wavelet packet bases are used to construct a secret frequency domain, leading to a situation in which only a minimal amount of data needs to be encrypted. Results and calculations are presented to evaluate the suggested approach in terms of compression performance, security, and applicability

Format-compliant encryption of JPEG2000 has attracted researchers for several years. Most benefits of format-compliant encryption result from the preservation of code-stream features, such as scalability. The possibility of reducing the complexity of encryption and to realize transparent encryption schemes are among the additional features of format-compliant selective encryption. Wu and Deng have proposed a format-compliant iterative encryption scheme for JPEG2000 on a CCP basis. In this paper we discuss whether it is possible to extend this approach to packet bodies and give an exact formula for the expected computational effort. The theoretical results are cross-verified by an experimental survey

In this paper we investigate the impact of using random coefficient permutation to provide confidentiality in wavelet-based still image compression pipelines. We compare the changes in compression performance of JPEG 2000 and SPIHT-based schemes and discuss key management scenarios. The results also have interesting implications with respect to the significance of the zerotree hypothesis.

We propose the use of random wavelet packet decompositions as a way to increase the security of watermarking systems against unauthorized removal attacks. Experimental attacks based on coefficient quantization show that using a secret key-dependent subband structure to hide the watermarking domain significantly increases the watermark correlation under an attack as compared to a classical pyramidal wavelet watermark.

A lightweight JPEG 2000 encryption scheme for mobile environments, based on wavelet filter parametrization, is discussed. Being a special variant of header encryption, the technique has an extremely low computational demand. Compression quality and security assessment show that the scheme may be employed in applications requiring a low to medium security level, provided the conditions for a secure use discussed in this work are obeyed

We propose to use secret, key-dependent, parametric, wavelet filters to improve the security of digital watermarking schemes operating in the wavelet transform domain. We show that the parametrization of wavelet filters can be easily integrated into existing wavelet-based watermarking algorithms, resulting in improved security without additional computational complexity. Both robustness and imperceptibility are adequate for many applications.

Although frequently used in fractal compression quadrant-based classification exhibits a significant defect which has not been described in the literature. We give an efficient solution to this problem by controlling the class structure based on an adaptive threshold. This makes this classification technique a very efficient and competitive one also for block matching motion compensation algorithms in video coding.

In this paper we describe and analyze algorithms for 2-D wavelet packet decomposition for MIMD distributed memory architectures. We discuss two different approaches: On the one hand algorithms generating the entire wavelet packet subband structure (as required for adaptive applications), on the other hand algorithms generating the lowest subband level only (as required for numerical applications). We investigate several optimizations and generalizations of corresponding message passing algorithms and finally compare the results obtained on a Cray T3D and a Parsytec GCel 1024.

Summary form only given. The authors introduce hybrid algorithms which consist of a fractal predictor in the spatial domain with subsequent coding of the residual image (error-image between the fractal prediction and the image to compress). For coding the residual either wavelet (based on the SPIHT coder) or DCT based coding (as used in interframe compression, e.g. for B or P frames in H.261, MPEG-1,2) is employed. Additionally they contribute to the discussion about the performance of wavelet and DCT based algorithms for the compression of motion compensated error frames in interframe video coding algorithms since the residual images considered in the proposed hybrid algorithms exhibit similar (or even identical) statistical properties as motion compensated error frames.

Adaptive wavelet-based image compression methods are introduced. In contrast to the classical wavelet decomposition scheme one can use different wavelet and scaling functions at every scale-this leads to nonstationary multiresolution analysis. An inhomogeneous multiresolution analysis is obtained by using different functions for the two directions in the tensor product of the bidimensional multiresolution analysis, furthermore these two methods are combined. The freedom in using different functions is exploited for image compression in an adaptive way. Special non-stationary and/or inhomogeneous multiresolution analysis is built out of the functions in a given library that is best suited for compression of a given image. This is done by minimizing cost-functions at every scale-level.>