Over the last decade, biometrics has witnessed significant advancements in various forensic and security applications for human identification and authentication, with growing interest in effective and discriminative traits such as palmprints. However, practical applications still face challenges, especially when palmprints are collected in portions, such as at crime scenes, or partially acquired for authentication in uncontrolled environments. This paper presents a novel method that incorporates the local binary patterns (LBP) operator into the conventional scale-invariant feature transform (SIFT) algorithm to detect and extract robust keypoint features. While SIFT employs a Gaussian filter to detect keypoints on the palmprint, the proposed method leverages the multi-scale LBP operator to detect stable points prior to computing the corresponding descriptors. Furthermore, an efficient method for filtering keypoints, namely the Self-Geometric Relationship (SGR) filter, is introduced to eliminate potential false matches. The proposed palmprint recognition system, LBPSIFT-SGR, demonstrates competitive performance on full palmprints compared to state-of-the-art techniques and exhibits clear superiority on partial palmprint images, where competing systems fail, across different datasets.

Over the last decade, videos uploaded and shared through web-based multimedia platforms and mobile applications have proliferated worldwide. This is because cloud-based applications such as iCloud, YouTube, Facebook, Twitter, and WhatsApp offer affordable and secure environments for video storage and sharing. However, new challenges have emerged alarming forensic analysts and investigators since videos can be used to commit heinous crimes such as blackmail, fraud, and forgery. Source Camera Identification (SCI) has become of paramount importance in the field of image and video forensics. Camera model identification can also help identify the perpetrators or narrow down the search and can be used to enhance SCI systems. In this context, existing approaches such as the Photo Response Non-Uniformity (PRNU) based methods and machine learning techniques such as the support vector machine (SVM) and deep learning models are commonly used solutions. This work exploits these two categories of methods by exploring a hierarchical deep learning model for camera model identification based on smartphone videos. The PRNU features are extracted by CNN-based structures during the training process. Proposed six-stream networks are leveraged to extract both low-level and high-level features through the network. A fusion layer is created based on joint sparse representation using forward and backward functions defined for fusing the proposed six streams. The proposed approach has been implemented and evaluated through intensive experiments, and results showed successful camera model identification with a performance at the frame level reaching an average accuracy of 69.9% for the Daxing dataset and 81.6% for the QUFVD dataset.

Video Source device identification plays a crucial role in video forensics as the proliferation of video capturing devices has given rise to crimes with videos that are challenging to trace. Reliance on metadata extraction is insufficient as it can be corrupted or manipulated to conceal the source of the crime. Another technique employed for source identification is noise pattern extraction, which generates a unique identification for the video camera. However, this method is susceptible to capture faults and can produce diverse noise patterns for each video. In addressing these challenges, there is a need to identify distinctive features that are consistent across all videos captured by the same camera. This has led to the adoption of computer vision techniques utilizing machine learning and deep learning. Classifiers play a crucial role in machine learning and data analysis, as they are responsible for categorizing or predicting results based on input data. Our experiments show that the subject is sensitive to classifiers and developing a good classifier or classifier-level fusions can improve results in practice for all datasets.

Forensic watermarking enables the tracing of digital pirates that leak copyright-protected multimedia. To prevent a negative impact on the video quality or bit rate, rate-distortion-preserving watermarking exists, which represents a watermark as compression artifacts. However, this method has two main disadvantages; the detection has a high complexity and it is non-blind. Although a method based on perceptual hashing exists that speeds up the detection of a fallback watermarking system, it decreases its robustness. Therefore, this paper proposes a novel fast detection method that has less impact on the robustness than related work. Our method optimized NS-DCT-DST hashes for rate-distortion-preserving watermarking, which are more robust to content-preserving attacks. Moreover, a blind version is proposed which does not require the original video for hash extraction. As such, the detection is experimentally measured to be up to 5700 times faster, at the cost of a modest decrease in robustness. In fact, the proposed method shows good robustness to content-preserving recompression attacks when using hashes that are as small as 432 bytes. This is much smaller than related work at comparable performance. In conclusion, this paper enables fast adversary tracing using watermarks that do not impact the video’s compression efficiency.

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing imperfections in the imaging device. The PRNU is a unique noise for each sensor device, and it has been generally utilized in the literature for source camera identification and image authentication. In video forensics, the traditional approach estimates the PRNU by averaging a set of residual signals obtained from multiple video frames. However, due to lossy compression and other non-unique content-dependent noise components that interfere with the video data, constant averaging does not take into account the intensity of these undesirable noise components which are content-dependent. Different from the traditional approach, we propose a video PRNU estimation method based on weighted averaging. The noise residual is first extracted for each single video. Then, the estimated noise residuals are fed into a weighted averaging method to optimize PRNU estimation. Experimental results on two video datasets captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art one.

Palm vein images usually suffer from low contrast due to skin surface scattering the radiance of NIR light and image sensor limitations, hence require employing various techniques to enhance the contrast of the image prior to feature extraction. This paper presents a novel image enhancement method referred to as Multiple Overlapping Tiles (MOT) which adaptively stretches the local contrast of palm vein images using multiple layers of overlapping image tiles. The experiments conducted on the CASIA palm vein image dataset demonstrate that the MOT method retains the finer subspace details of vein images which allows excellent feature detection and matching with SIFT and RootSIFT features. Results on existing palm vein recognition systems demonstrate that the proposed MOT method delivers lower EER values outperforming other existing palm vein image enhancement methods.

Recent advances in digital imaging have meant that every smartphone has a video camera that can record high-quality video for free and without restrictions. In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide. One of the security issues is the identification of source cameras on videos. Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and Machine Learning approaches. To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net. To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera. The experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models. On average, the network improves the results in a new database by about 4%.

This paper introduces a standard digital picture dataset specifically designed for temporal digital image forensics. The database, called Northumbria Temporal Image Forensics (NTIF), consists of natural images with full high resolution of indoor and outdoor scenes. The images are organized in temporal order with regular acquisition timeslots spanned over for 94 weeks using ten digital camera devices. 41,684 images were captured from 10 digital cameras belonged to different models and brands. To this end, the subset of images has been annotated with labels spanning over categories based on the temporal factor of one to two weeks. Constructing such a large-scale temporal image database has been a challenging and enduring process. During the construction of NTIF, ethics were fully considered. The proposed dataset will be freely accessible to benefit all researchers in image forensics from academia and industry. This paper aims to describe the NTIF database and highlight the changes in Sensor Pattern Noise over time. Experiments have been conducted in which the correlations between noise residuals appear to be sensitive to the acquisition time of the respective digital images. The results show a clearly different pattern of correlations when the images are captured in different timeslots as compared to those images acquired within the same timeslots.

Abstract Over the past two decades, researchers in the field of biometrics have presented a wide variety of coding-based palmprint recognition methods. These approaches mainly rely on extracting the texture features, e.g. line orientations, and phase information, using different filters. In this paper, we propose a new efficient palmprint recognition method based on the Different of Block Means. In the proposed scheme, only basic operations (i.e. mainly additions and subtractions) are used, thus involving a much lower computational cost when compared with existing systems. This makes the system suitable for online palmprint identification and verification. Furthermore, the technique has been shown to deliver superior performance over related systems.

Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions.

Handwriting biometrics is the science of identifying the behavioral aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines scale-invariant feature transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMMs). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While an SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates an SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic, and one hybrid language), and the results have shown the superiority of the proposed system over the state-of-the-art techniques.

In this paper, a robust additive image watermarking system operating in the Dual Tree Complex Wavelet Transform (DTCWT) domain is proposed. The system takes advantage of a new perceptual masking model that exploits the Human Visual System (HVS) characteristics at the embedding stage. It also uses an efficient watermark detection structure, called the Rao-test, to verify the presence of the candidate watermark. This structure relies on the statistical modeling of high frequency DTCWT coefficients by the Generalized Gaussian distribution. Experimental results show that the proposed system outperforms related state-of-the-art watermarking systems in terms of imperceptibility and robustness.

Retinal image analysis may disclose severity and causes of many diabetic diseases e.g. for Diabetic Macular Edema inspection. Many techniques have been introduced for automatic classification of exudate lesion to speed up the diagnosis of diabetic disease. In almost all previous work, exudate lesion detection is either modelled as binary or multiclass classification problem. However, along with the classification of normal / abnormal regions, other information needs to be simultaneously classified such as patient's age, ethnicity, race, diabetic's type etc. In this work, we presented a new technique, namely Multi-label learning model to improve the classification of exudate lesions. Features are extracted using multi-scale local binary patterns. Multi label k nearest neighbour (ML-kNN), Multi-label Ranking Support Vector Machine Learning (ML-Rank SVM), Multi-label Learning Neural Network Radial Base Function (MLNN-RBF) and Multi-label Learning Neural Network Back-Propagation (MLNN-BP) are evaluated as the classification models and compared with traditional binary multi-class classifiers. Experiment results show that multi-label framework is very useful for diabetic retinopathy differentiation and can improve retinal image classification.

Extracting a fingerprint of a digital camera has fertile applications in image forensics, such as source camera identification and image authentication. In the last decade, photo response nonuniformity (PRNU) has been well established as a reliable unique fingerprint of digital imaging devices. The PRNU noise appears in every image as a very weak signal, and its reliable estimation is crucial for the success rate of the forensic application. In this paper, we present a novel methodical evaluation of 21 state-of-the-art PRNU estimation/enhancement techniques that have been proposed in the literature in various frameworks. The techniques are classified and systematically compared based on their role/stage in the PRNU estimation procedure, manifesting their intrinsic impacts. The performance of each technique is extensively demonstrated over a large-scale experiment to conclude this case-sensitive study. The experiments have been conducted on our created database and a public image database, the “Dresden image database.”

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for source camera identification and image authentication. The abundant information that the sensor pattern noise carries in terms of the frequency content makes it unique, and hence suitable for identifying the source camera and detecting image forgeries. However, the PRNU extraction process is inevitably faced with the presence of image-dependent information as well as other non-unique noise components. To reduce such undesirable effects, researchers have developed a number of techniques in different stages of the process, i.e., the filtering stage, the estimation stage, and the post-estimation stage. In this paper, we present a new PRNU-based source camera identification and verification system and propose enhancements in different stages. First, an improved version of the locally adaptive discrete cosine transform filter is proposed in the filtering stage. In the estimation stage, a new weighted averaging technique is presented. The post-estimation stage consists of concatenating the PRNUs estimated from color planes in order to exploit the presence of physical PRNU components in different channels. Experimental results on two image data sets acquired by various camera devices have shown a significant gain obtained with the proposed enhancements in each stage as well as the superiority of the overall system over related state-of-the-art systems.

With a rapidly increasing incidence of melanoma skin cancer, there is a need for decision support systems to detect it in its early stages, which would lead to better decisions in treating it successfully. However, developing such systems is still a challenging task for researchers. Several Computer Aided-Diagnosis (CAD) systems have been proposed in the last two decades to increase the accuracy of melanoma detection. Image feature extraction is a critical step in differentiating between melanoma and normal skin lesions. In this paper, we propose to improve a bag-of-words approach by combining features consisting of the color histogram and first order moments with the Histogram of Oriented Gradients (HOG). Experimental results show that the proposed technique significantly improves the detection accuracy, with an average sensitivity of 91% and specificity of 85%. The proposed system was validated on a dataset of 200 medically annotated images (40 melanomas and 160 non-melanomas) obtained from the database of the Hospital Pedro Hispano. [1].

Diabetic retinopathy (DR) is one of the most important cause of vision loss in diabetic patients. The most primary sign of DR is the presence of exudates, and detecting these in early screening is crucial in preventing vision loss. This paper proposes a system for automatic exudate detection using a combination of texture features, extracted from different local binary pattern (LBP) variants, with an artificial neural network (ANN) classifier. The publicly available database DIARETDB0 of colour fundus images was used for testing purposes and the values of sensitivity, specificity and accuracy found were 98.68%, 94.81 % and 96.73% respectively for the neural network based classification. These results have also been shown to outperform existing work.

Photo-response non-uniformity (PRNU) noise has been well established as a reliable fingerprint of imaging sensors for source camera identification and other forensic applications. In this paper, we introduce a novel denoising method for PRNU noise extraction that considerably outperforms the existing techniques. The rationale is to use as little as one adjacent pixel in spatial domain filtering to suppress the pixel-to-pixel correlation in the estimation of the (supposedly white) PRNU noise. Experimental results are presented based on an image database of ten cameras of different models and makes. These results are embodied in the receiver operating characteristic (ROC) of source camera identification.

Segmentation is considered as a core step for any recognition or classification method and for the text within any document to be effectively recognized it must be segmented accurately. In this paper a text and writer independent algorithm for the segmentation of sub-words in Arabic words has been presented. The concept is based around the global binarization of an image at various thresholding levels. When each sub-word or Part of Arabic Word (PAW) within the image being investigated is processed at multiple threshold levels a cluster graph is obtained where each cluster represents the individual sub-words of that word. Once the clusters are obtained the task of segmentation is managed by simply selecting the respective cluster automatically which is achieved using the 95% confidence interval on the processed data generated by the accumulated graph. The presented algorithm was tested on 537 randomly selected words from the AHTID/MW database and the results showed that 95.3% of the sub-words or PAW were correctly segmented and extracted. The proposed method has shown considerable improvement over the projection profile method which is commonly used to segment sub-words or PAW.

This paper proposes a robust minutiae based fingerprint image hashing technique. The idea is to incorporate the orientation and descriptor in the minutiae of fingerprint images using SIFT-Harris feature points. A recent shape context based perceptual hashing method has been compared against the proposed technique. Experimentally, the proposed technique has been shown to deliver better robustness against image processing operations including JPEG lossy compression and geometric attacks such as rotation and translation.

Sensor pattern noise has been broadly used in the literature for image authentication and source camera identification. The abundant information that a sensor pattern noise carries in terms of the frequency content makes it unique and hence suitable for source camera identification. The traditional approach for estimating the sensor pattern noise uses a set of images to estimate a pattern residual signal from each image. The estimated residual signals are then averaged to obtain the sensor pattern noise. This is based on the assumption that each residual signal is a noisy observation of the sensor pattern noise. Such an assumption is well justified in practice because the images are acquired under different conditions making the corresponding residual signals distinct from each other. For instance bright images provide better sensor pattern noise estimation than dark images. Also, saturated pixels cause undesirable noise in residual signals. Inspired by this observation, a weighted averaging approach is proposed for efficient sensor pattern noise estimation. The proposed approach has been validated with two sensor pattern noise estimation techniques from the literature and significant improvements have been shown through experimental results.

In this study, a robust wavelet‐based fingerprint image watermarking scheme using an efficient just perceptual weighting (JPW) model has been proposed. The JPW model exploits three human visual system characteristics, namely: spatial frequency sensitivity, local brightness masking and texture masking, to compute a weight for each wavelet coefficient, which is then used to control the amplitude of the inserted watermark. The idea is motivated by the fact that fingerprint images perceptually differ from natural images and a JPW model adapted to such images would further enhance the robustness of the watermarking scheme. Experimental results show that the proposed model significantly improves the performance of the conventional watermarking technique in terms of robustness while maintaining the same imperceptibility of the watermark. Finally, the proposed technique has shown a clear superiority over a number of related state‐of‐the‐art masking techniques.

This study proposes an efficient algorithm for colour image compression with listless implementation based on set partition block embedded coding (SPECK). The objective of this work is to develop an algorithm that exploits the redundancy in colour spaces, low complexity quadtree partitioning and reduced memory requirements. Colour images are first transformed into luminance chrominance (YCbCr) planes and a wavelet transform is applied. A reduction of the memory requirement is achieved with the introduction of a state marker that matches each colour plane to eliminate the list with dynamic memory in the original colour SPECK coder (CSPECK). The wavelet coefficients are scanned using Z ‐order that matches the subband decompositions. The proposed algorithm then encodes the de‐correlated colour plane as one unit and generates a mixed bit stream. The linear indexing and initial state marker are modified to jointly test the chrominance plane together. Composite colour coding enables precise control of the bit rate. The performance of the proposed algorithm is comparable with CSPECK, set partitioning in hierarchical trees (SPIHT) and JPEG2000 but with less memory requirements. For progressive lossless, a saving of more than 70% than final working memory against CSPECK and SPIHT highlights the benefit of the proposed algorithm.

This correspondence presents an iterative method based upon k -nearest neighbors ( k-NN) regression to improve the performance of statistical feature extraction for texture image retrieval. The idea exploits the fact that an ideal feature extraction system would extract similar signatures from images characterized by the same texture and different signatures from dissimilar textures. Under the assumption that conventional statistical feature extraction contributes to sufficiently good retrieval performance, the signatures of k retrieved textures are used to update the signature of the query image using the k -NN regression algorithm. Extensive experiments show significant improvements with respect to retrieval performance in comparison to conventional statistical feature extraction.

In this letter, we analyze the security of a perceptual image hashing technique based on non-negative matrix factorization which was recently proposed and reported in the literature. We theoretically demonstrate that, although the technique uses different secret keys in subsequent stages, the first key plays an essential role to secure the hashing system. We next act as an attacker and propose a technique to estimate the secret key. Extensive experiments support our theoretical analysis and validate the proposed key estimation technique.

This paper proposes a new robust and secure perceptual image hashing technique based on virtual watermark detection. The idea is justified by the fact that the watermark detector responds similarly to perceptually close images using a non embedded watermark. The hash values are extracted in binary form with a perfect control over the probability distribution of the hash bits. Moreover, a key is used to generate pseudo-random noise whose real values contribute to the randomness of the feature vector with a significantly increased uncertainty of the adversary, measured by mutual information, in comparison with linear correlation. Experimentally, the proposed technique has been shown to outperform related state-of-the art techniques recently proposed in the literature in terms of robustness with respect to image processing manipulations and geometric attacks.

This paper describes an efficient watermarking technique for use to protect fingerprint images. The rationale is to embed the watermarks into the ridges area of the fingerprint images so that the technique is inherently robust, yields imperceptible watermarks, and resists well against cropping and/or segmentation attacks. The proposed technique improves the performance of optimum multibit watermark decoding, based on the maximum likelihood scheme and the statistical properties of the host data. The technique has been applied successfully on the well-known transform domains: discrete cosine transform (DCT) and discrete wavelet transform (DWT). The statistical properties of the coefficients from the two transforms are modeled by a generalized Gaussian model, widely adopted in the literature. The results obtained are very attractive and clearly show significant improvements when compared to the conventional technique, which operates on the whole image. Also, the results suggest that the segmentation (cropping) attack does not affect the performance of the proposed technique, which also provides more robustness against other common attacks.

This letter proposes an efficient extension of the set partitioning embedded block (SPECK) algorithm to lossless multispectral image coding. Such a wavelet-based coder is widely referred to in the literature, especially for lossless image coding, and is considered to be one of the most efficient techniques exhibiting very low computational complexity when compared with other state-of-the-art coders. The modification proposed in this letter is simple and provides significant improvement over the conventional SPECK. The key idea is to join each group of two consecutive wavelet-transformed spectral bands during the SPECK coding since they show high similarities with respect to insignificant sets at the same locations. Simulation results, carried out on a number of test images, demonstrate that this grouping procedure considerably saves on the bit budget for encoding the multispectral images.

In this paper, the compression of multispectral images is addressed. Such 3-D data are characterized by a high correlation across the spectral components. The efficiency of the state-of-the-art wavelet-based coder 3-D SPIHT is considered. Although the 3-D SPIHT algorithm provides the obvious way to process a multispectral image as a volumetric block and, consequently, maintain the attractive properties exhibited in 2-D (excellent performance, low complexity, and embeddedness of the bit-stream), its 3-D trees structure is shown to be not adequately suited for 3-D wavelet transformed (DWT) multispectral images. The fact that each parent has eight children in the 3-D structure considerably increases the list of insignificant sets (LIS) and the list of insignificant pixels (LIP) since the partitioning of any set produces eight subsets which will be processed similarly during the sorting pass. Thus, a significant portion from the overall bit-budget is wastedly spent to sort insignificant information. Through an investigation based on results analysis, we demonstrate that a straightforward 2-D SPIHT technique, when suitably adjusted to maintain the rate scalability and carried out in the 3-D DWT domain, overcomes this weakness. In addition, a new SPIHT-based scalable multispectral image compression algorithm is used in the initial iterations to exploit the redundancies within each group of two consecutive spectral bands. Numerical experiments on a number of multispectral images have shown that the proposed scheme provides significant improvements over related works.

This paper presents an investigation on the optimum detection of multiplicative watermarks based on statistical behaviour of image contents in the transform domain. In recent works, the problem of watermark detection is viewed as a binary decision where the observation is the possibly watermarked transformed coefficients. Indeed, the detector verifies whether the watermark presented to its input is actually embedded in the input image (hypothesis HI) or not (hypothesis HQ). Such a detection scheme relies on the Neyman-Pearson criterion to derive a decision threshold by minimising the probability of missed detection with respect to a given probability of false alarm. Previous works approximate the probability density function (pdf) of the observation when hypothesis HQ holds by the pdf when hypothesis of having no watermark embedded in the input image is in force by assuming that the watermark strength is weak to some extent. However, the weakness of the watermark does fulfil the requirement on the robustness. Moreover, from the viewpoint of the decision theory, the smaller the embedding depth, the worse the watermark detection. This paper describes the drawback behind this approximation in the general case and proposes an efficient solution closer to the theoretical derivation. To validate the proposed technique, we consider a special case in which the Laplace statistical model is used.

This paper proposes an adaptive blind image watermarking technique based on wavelet transform using a random sequence of real numbers. First, the image is decomposed into non overlapping blocks. Then, each block is classified as uniform or non uniform by using a JND-based classifier. The strength of the embedded watermark into the high subband coefficients of each transformed block depends upon the nature of the block according to its classification. The Neyman-Pearson criterion is used to derive the detection rule. Unlike the embedding process, the detection does not require any classification of blocs. This adaptive approach has been assessed on various standard images and compared with a similar watermarking technique (H. Inoue et al., 1999). The results have shown an obvious improvement in terms of robustness against different manipulations and a better ability of detection

We propose an efficient extension of set partitioning in hierarchical trees (SPIHT) for very low bit-rate wavelet based color image coding. Since the chrominance components I and Q in the YIQ format are sufficiently less significant in terms of energy compared to the luminance component, the trees within each chrominance plane are joined together in the list of insignificant sets (LIS) according to a virtual relationship parent-descendants specific to the chrominance components. For generating a fully embedded bit stream similar to SPIHT, the proposed method improves the performance of the color SPIHT based scheme, especially for very low bit rate.