Federated Learning (FL) enables collaborative model training across distributed clients without exposing raw privacy-sensitive data. To ensure confidentiality against honest-but-curious aggregation servers, Additive Homomorphic Encryption, such as the Paillier cryptosystem, is widely deployed. However, preserving the integrity and authenticity of the homomorphically encrypted local updates remains a critical challenge. Traditional cryptographic signatures introduce significant communication overhead and metadata management complexities, which exacerbate the inherent bandwidth bottlenecks of FL. In this paper, we introduce a metadata-free integrity protection scheme based on Deterministic Self-Blinding (DSB). By deterministically re-randomizing specific Paillier ciphertexts to encode bits via interval membership, DSB allows clients to embed a complete digital signature directly within the encrypted model updates. This operation requires a constant-time (O(1)) modular subtraction per signature bit, entirely avoiding the computationally expensive random searches of Probabilistic Self-Blinding approaches. Furthermore, we formalize the security of the DSB mechanism through a rigorous adversary-challenger reduction, proving that it preserves the IND-CPA security of Paillier while ensuring strong EUF-CMA unforgeability against active network adversaries. Experimental results demonstrate that our scheme achieves strict zero communication overhead and negligible computation latency without degrading the global model’s accuracy. Our protocol offers a highly efficient, framework-agnostic security foundation that can be seamlessly integrated into production-grade privacy-preserving FL deployments.

White-box watermarking embeds ownership signatures directly into DNN parameters, yet it faces a critical blind spot: topology-altering obfuscation. By modifying a model’s internal structure while preserving its input-output behavior, an attacker can misalign the watermark from its expected parameter locations, causing standard white-box extractors to fail. We investigate whether existing white-box watermarks remain verifiable after such attacks by introducing Distillation-as-Defense: rather than reversing the obfuscation, we distill the obfuscated model (Teacher) into a student with the original architecture, forcing it to reconstruct the functional watermark representation. We systematically evaluate ten white-box watermarking schemes—eight static (weight-based) and two dynamic (activation-based)—across classifiers, generative models, and transformers. Dynamic methods consistently recover their watermarks under feature-map alignment distillation, while most static methods fail on deep architectures due to internal representation redundancy. These findings reveal that current white-box schemes were not designed with distillation robustness in mind. We conclude that resistance to distillation is a necessary condition for a white-box watermark to withstand topology-altering obfuscation, and we discuss a concrete design guidelines toward this goal.

Computed tomography (CT) scanners are widely used to obtain detailed internal images in clinical diagnosis. Highly attenuated metallic implants resulting from strong and energy-dependent attenuation cause metal artifacts in CT scanning. However, current supervised deep network-based metal artifact reduction (MAR) methods hardly generalize in clinical diagnosis and treatment because of difficult acquisition for the paired artifact-affected and artifact-free data. In addition, these deep model-based methods cannot ensure the sinogram-domain data consistency for the exact metal trace inpainting. To address the above problems, we propose an UnsuPervised sinoGRam-domAin Data-consistEnt network for MAR, i.e., UPGRADE-Net. First, UPGRADE-Net fully leverages the prior knowledge to guide the generative conditional diffusion model for fine-grained metal trace inpainting. Second, without the artifact-free ground truth, a deep unsupervised MAR framework in the reverse process is constructed to contextually learn the known background data distribution for the unknown metal trace restoration in sinogram-domain. Third, to further maintain the sinogram-domain data consistency, two physics-based consistency constraint loss functions, including conjugate-ray and accumulation-ray consistency loss, are designed for the conjugate point constraint and the accumulation constraint. The proposed UPGRADE-Net is trained and evaluated on a publicly available dataset and a clinical dataset. Extensive experimental results validate that the proposed method outperforms the state-of-the-art competing methods for MAR.

4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods heavily rely on large labeled training datasets which are difficult to obtain in practical scenarios. Restricted by this dilemma, DL models often struggle with simultaneously retaining dynamic motions, removing streak degradations, and recovering fine details. To address the above challenging problem, we introduce a Deep Prior Image Constrained Motion Compensation framework (DPI-MoCo) that decouples the 4D CBCT reconstruction into two sub-tasks including coarse image restoration and structural detail fine-tuning. In the first stage, the proposed DPI-MoCo combines the prior image guidance, generative adversarial network, and contrastive learning to globally suppress the artifacts while maintaining the respiratory movements. After that, to further enhance the local anatomical structures, the motion estimation and compensation technique is adopted. Notably, our framework is performed without the need for paired datasets, ensuring practicality in clinical cases. In the Monte Carlo simulation dataset, the DPI-MoCo achieves competitive quantitative performance compared to the state-of-the-art (SOTA) methods. Furthermore, we test DPI-MoCo in clinical lung cancer datasets, and experiments validate that DPI-MoCo not only restores small anatomical structures and lesions but also preserves motion information.

International audience

Internet of Medical Things (IoMT) and telemedicine technologies utilize computers, communications, and medical devices to facilitate off-site exchanges between specialists and patients, specialists, and medical staff. If the information communicated in IoMT is illegally steganography, tampered or leaked during transmission and storage, it will directly impact patient privacy or the consultation results with possible serious medical incidents. Steganalysis is of great significance for the identification of medical images transmitted illegally in IoMT and telemedicine. In this article, we propose a Residual and Enhanced Discriminative Network (RED-Net) for image steganalysis in the internet of medical things and telemedicine. RED-Net consists of a steganographic information enhancement module, a deep residual network, and steganographic information discriminative mechanism. Specifically, a steganographic information enhancement module is adopted by the RED-Net to boost the illegal steganographic signal in texturally complex high-dimensional medical image features. A deep residual network is utilized for steganographic feature extraction and compression. A steganographic information discriminative mechanism is employed by the deep residual network to enable it to recalibrate the steganographic features and drop high-frequency features that are mistaken for steganographic information. Experiments conducted on public and private datasets with data hiding payloads ranging from 0.1bpp/bpnzac-0.5bpp/bpnzac in the spatial and JPEG domain led to RED-Net's steganalysis error$P_{\mathrm{E}}$in the range of 0.0732-0.0010 and 0.231-0.026, respectively. In general, qualitative and quantitative results on public and private datasets demonstrate that the RED-Net outperforms 8 state-of-art steganography detectors.

Quantitative infarct estimation is crucial for diagnosis, treatment and prognosis in acute ischemic stroke (AIS) patients. As the early changes of ischemic tissue are subtle and easily confounded by normal brain tissue, it remains a very challenging task. However, existing methods often ignore or confuse the contribution of different types of anatomical asymmetry caused by intrinsic and pathological changes to segmentation. Further, inefficient domain knowledge utilization leads to mis-segmentation for AIS infarcts. Inspired by this idea, we propose a pathological asymmetry-guided progressive learning (PAPL) method for AIS infarct segmentation. PAPL mimics the step-by-step learning patterns observed in humans, including three progressive stages: knowledge preparation stage, formal learning stage, and examination improvement stage. First, knowledge preparation stage accumulates the preparatory domain knowledge of the infarct segmentation task, helping to learn domain-specific knowledge representations to enhance the discriminative ability for pathological asymmetries by constructed contrastive learning task. Then, formal learning stage efficiently performs end-to-end training guided by learned knowledge representations, in which the designed feature compensation module (FCM) can leverage the anatomy similarity between adjacent slices from the volumetric medical image to help aggregate rich anatomical context information. Finally, examination improvement stage encourages improving the infarct prediction from the previous stage, where the proposed perception refinement strategy (RPRS) further exploits the bilateral difference comparison to correct the mis-segmentation infarct regions by adaptively regional shrink and expansion. Extensive experiments on public and in-house NCCT datasets demonstrated the superiority of the proposed PAPL, which is promising to help better stroke evaluation and treatment.

Ensuring the authenticity of scanned documents is of major concern, these ones being often admitted as evidence by organizations. “Is there any way to verify that a document was scanned by a device without having access physically to the source device itself?” is a wide-open question. In this paper, we aim at answering it in the affirmative by means of the first data-driven hybrid machine learning framework that compares image noise features to check if two documents have been digitized with the same scanner or not. Such a problem is known as device linking function. Different comparative experiments conducted on the same and different scanner models on a broad set of administrative documents demonstrate that our method is efficient in linking scanned images even if scanner devices are unknown to the investigator. Our success rate of 96% appears to be the novel state of art reference in such application domain.

Low-dose computed tomography (LDCT) imaging faces great challenges. Although supervised learning has revealed great potential, it requires sufficient and high-quality references for network training. Therefore, existing deep learning methods have been sparingly applied in clinical practice. To this end, this paper presents a novel Unsharp Structure Guided Filtering (USGF) method, which can reconstruct high-quality CT images directly from low-dose projections without clean references. Specifically, we first employ low-pass filters to estimate the structure priors from the input LDCT images. Then, inspired by classical structure transfer techniques, deep convolutional networks are adopted to implement our imaging method which combines guided filtering and structure transfer. Finally, the structure priors serve as the guidance images to alleviate over-smoothing, as they can transfer specific structural characteristics to the generated images. Furthermore, we incorporate traditional FBP algorithms into self-supervised training to enable the transformation of projection domain data to the image domain. Extensive comparisons and analyses on three datasets demonstrate that the proposed USGF has achieved superior performance in terms of noise suppression and edge preservation, and could have a significant impact on LDCT imaging in the future.

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Modeling when the exposure moment appears. A large-scale dataset for X-ray examination scene is built to validate the performance of the proposed method. Extensive experiments demonstrate the superiority of the proposed method in automatically recognizing the exposure moment and exposure region. This paradigm provides the first method that can enable automatically and accurately recognize the exposure region in X-ray imaging without the help of the radiographer.

X-ray computed tomography (CT) is of great clinical significance in medical practice because it can provide anatomical information about the human body without invasion, while its radiation risk has continued to attract public concerns. Reducing the radiation dose may induce noise and artifacts to the reconstructed images, which will interfere with the judgments of radiologists. Previous studies have confirmed that deep learning (DL) is promising for improving low-dose CT imaging. However, almost all the DL-based methods suffer from subtle structure degeneration and blurring effect after aggressive denoising, which has become the general challenging issue. This paper develops the Comprehensive Learning Enabled Adversarial Reconstruction (CLEAR) method to tackle the above problems. CLEAR achieves subtle structure enhanced low-dose CT imaging through a progressive improvement strategy. First, the generator established on the comprehensive domain can extract more features than the one built on degraded CT images and directly map raw projections to high-quality CT images, which is significantly different from the routine GAN practice. Second, a multi-level loss is assigned to the generator to push all the network components to be updated towards high-quality reconstruction, preserving the consistency between generated images and gold-standard images. Finally, following the WGAN-GP modality, CLEAR can migrate the real statistical properties to the generated images to alleviate over-smoothing. Qualitative and quantitative analyses have demonstrated the competitive performance of CLEAR in terms of noise suppression, structural fidelity and visual perception improvement.

In this paper, we improve the parallel deep neural network (DNN) scheme BusterNet for image copy-move forgery localization with source/target region distinguishment. BusterNet is based on two branches, i.e., Simi-Det and Mani-Det, and suffers from two main drawbacks: (a) it should ensure that both branches correctly locate regions; (b) the Simi-Det branch only extracts single-level and low-resolution features using VGG16 with four pooling layers. To ensure the identification of the source and target regions, we introduce two subnetworks that are constructed serially: the copy-move similarity detection network (CMSDNet) and the source/target region distinguishment network (STRDNet). Regarding the second drawback, the CMSDNet subnetwork improves Simi-Det by removing the last pooling layer in VGG16 and by introducing atrous convolution into VGG16 to preserve field-of-views of filters after the removal of the fourth pooling layer; double-level self-correlation is also considered for matching hierarchical features. Moreover, atrous spatial pyramid pooling and attention mechanism allow the capture of multiscale features and provide evidence for important information. Finally, STRDNet is designed to determine the similar regions obtained from CMSDNet directly as tampered regions and untampered regions. It determines regions at the image-level rather than at the pixel-level as made by Mani-Det of BusterNet. Experimental results on four publicly available datasets (new synthetic dataset, CASIA, CoMoFoD, and COVERAGE) demonstrate that the proposed algorithm is superior to the state-of-the-art algorithms in terms of similarity detection ability and source/target distinguishment ability.

In this paper, we study the possibility to feed machine learning models with JPEG compressed images during their training phase. The underlying objective is to evaluate if JPEG decompression can be avoided so as to gain in computation time and, if yes, at what price in terms of model accuracy. To do so, we trained two well-known machine learning models: Neural Networks (NN) and Convolutional Neural Network (CNN), with pieces of data issued from different steps of the partial JPEG decompression of images. We analyze how such partially decompressed JPEG data influence machine learning model accuracy. We also study the impact of the JPEG quality factor. Experiments conducted on two image databases, MNIST database and CIFAR-10 database, show that the learning task complexity of NN models can be reduced working with partially decompressed images with a low model accuracy loss, while for CNN model's accuracy loss depends on the JPEG data. A trade-off has to be found. With a quality factor of 80, the decompression computation complexity gain is of 45% for an accuracy loss of 13%. This work also point out the need for model adapted to compressed data.

The ease of use and the capabilities of image editing tools has raised the challenges in the emerging field of digital image forensics related to scanned documents. Unfortunately, the universality of current methods and their applicability in real world scenarios have not been proven yet due to the absence of a standardized image database. In this paper, we introduce a novel test collection of more than 4500 images annotated with respect to each scanner as a useful tool for forensics in-vestigators to test and compare scanner-based forensic techniques. It is an image database that contains document of various content scanned with more than one resolution with 11 different scanner instances of widely known brands. This selection is based on our latest work on the identification of scanners at the origin of digitized documents and is adapted to fit any source scanner identification technique. The SUPATLANTIQUE database is available for free to the research community and is intended to become a useful and a reference resource for researchers in this field.

In this paper, we propose the first joint watermarking-encryption-compression scheme for the purpose of protecting of medical images. The main originality of this scheme stands on its ability to give access to watermarking-based security services from both encrypted and compressed image bitstreams without having to decrypt or to decompress them, even partially. More clearly, there is no need neither to decrypt the encrypted image bitstream nor to decode the compressed image bitstream in order to extract watermarks. A second contribution is that it combines in a single algorithm the bit substitution watermarking modulation with JPEG-LS and the AES block cipher algorithm in its CBC mode. On their side, decompression, decryption and message extraction are conducted separately. Doing so makes our scheme compliant to the medical image standard DICOM. This scheme allows tracing images and controlling their reliability (i.e. based on proofs of image integrity and authenticity) either from the encrypted domain or from the compressed one. Experiments conducted on broad sets of Retina and ultrasound medical images demonstrate the capability of our system to securely make available a message in both encrypted and compressed domains while minimizing image distortion. Achieved watermarking capacities are large enough to support several watermarking-based security services at the same time.

In neuroscience research, brain tissue segmentation from magnetic resonance imaging is of significant importance. A challenging issue is to provide an accurate segmentation due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. To overcome these problems, we propose a novel brain MRI segmentation algorithm, the originality of which stands on the combination of supervoxels with graph convolutional networks. Supervoxels are generated from the 3D MRI image with the help of an improved simple linear iterative clustering algorithm. A graph is then built from these supervoxels through the K nearest neighbor algorithm, before being sent to GCNs for tissues classification. The proposed method is evaluated on the two common datasets- the BrainWeb18 dataset and the Internet Brain Segmentation Repository 18 dataset. Experiments demonstrate the performance of our method and that it is better than well-known state-of-the-art methods such as FMRIB software library, statistical parametric mapping, adaptive graph filter.

In this paper, we propose the first joint watermarking-encryption-compression scheme for the protection of medical images. Its originality is twofold. In a first time, it allows the access to watermarking-based security services from the encrypted and the compressed bitstreams without having to parse them even partially. It becomes possible to trace images and control their reliability (i.e. integrity and authenticity) from both the encrypted and compressed domains. In a second, it stands on the combination of bit-substitution watermarking, JPEG-LS and the AES block cipher in its CBC mode so as to make our scheme compliant with DICOM. Experiments conducted on different medical images modalities (radiographic and retina images) demonstrate the capability of our system to securely make available a message in both encrypted and compressed domains while minimizing the distortion of the image.

Low-dose computed tomography (LDCT) images are often highly degraded by amplified mottle noise and streak artifacts. Maintaining image quality under low-dose scan protocols is a well-known challenge. Recently, sparse representation-based techniques have been shown to be efficient in improving such CT images. In this paper, we propose a 3D feature constrained reconstruction (3D-FCR) algorithm for LDCT image reconstruction. The feature information used in the 3D-FCR algorithm relies on a 3D feature dictionary constructed from available high quality standard-dose CT sample. The CT voxels and the sparse coefficients are sequentially updated using an alternating minimization scheme. The performance of the 3D-FCR algorithm was assessed through experiments conducted on phantom simulation data and clinical data. A comparison with previously reported solutions was also performed. Qualitative and quantitative results show that the proposed method can lead to a promising improvement of LDCT image quality.

In this paper, we propose an homomorphic proxy re-encryption scheme (HPRE) that allows different users to share data they outsourced homomorphically encrypted using their respective public keys with the possibility by next to process such data remotely. Its originality stands on a solution we propose so as to compute the difference of data encrypted with Damgard-Jurik cryptosystem. It takes also advantage of a secure combined linear congruential generator that we implemented in the Damgard-Jurik encrypted domain. Basically, in our HPRE scheme, the two users, the delegator and the delegate, ask the cloud server to generate an encrypted noise based on a secret key, both users previously agreed on. Based on our solution to compute the difference in Damgard-Jurik encrypted domain, the cloud computes in clear the differences in-between the encrypted noise and the encrypted data of the delegator, obtaining thus blinded data. In order the delegate gets access to the data, the cloud just has to encrypt these differences using the delegate's public key and then removes the noise. This solution doesn't need extra communication between the cloud and the delegator. Our HPRE was implemented in the case of the sharing of uncompressed images stored in the cloud showing good time computation performance, it is unidirectional and collusion-resistant. Nevertheless, it is not limited to images and can be used with any kinds of data.

In this paper, we focus on the "blind" identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94% and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.

In low dose computed tomography (LDCT) imaging, the data inconsistency of measured noisy projections can significantly deteriorate reconstruction images. To deal with this problem, we propose here a new sinogram restoration approach, the sinogram- discriminative feature representation (S-DFR) method. Different from other sinogram restoration methods, the proposed method works through a 3-D representation-based feature decomposition of the projected attenuation component and the noise component using a well-designed composite dictionary containing atoms with discriminative features. This method can be easily implemented with good robustness in parameter setting. Its comparison to other competing methods through experiments on simulated and real data demonstrated that the S-DFR method offers a sound alternative in LDCT.

The papers in this special section were presented at three well-known conferences organized in 2014: EAI Mobihealth, IEEE HealthCom, and IEEE Biomedical and Health Informatics. EAI Mobihealth is an annually organized conference, which started in 2010, to address the demands of the rapidly evolving disciplines of wireless communications, mobile computing, and sensing technologies in healthcare. The IEEE-Healthcom is held every year since 1999 in different countries in Asia, Europe, and in America. It aims at bringing together interested parties working in the field of healthcare to exchange ideas, discuss innovative and emerging solutions, and develop collaborations. The IEEE Biomedical Health Informatics Conference started in 2013 and is organized every year providing the forum to showcase enabling technologies of computing, devices, imaging, sensors, and systems that optimize the acquisition, transmission, processing, storage, retrieval, visualization, and analysis of medical data. The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients, security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.

This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, breasts are first partitioned adaptively into regions. Then, features derived from the detection of lesions (masses and microcalcifications) as well as textural features, are extracted from each region and combined in order to classify mammography examinations as "normal" or "abnormal". Whenever an abnormal examination record is detected, the regions that induced that automated diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index. In a second scenario, the local and global anomaly detectors are trained simultaneously, without manual segmentations, using various MIL algorithms (DD, APR, mi-SVM, MI-SVM and MILBoost). Experiments on the DDSM dataset show that the second approach, which is only weakly-supervised, surprisingly outperforms the first approach, even though it is strongly-supervised. This suggests that anomaly detectors can be advantageously trained on large medical image archives, without the need for manual segmentation.

In this paper, we present a new robust database watermarking scheme the originality of which stands on a semantic control of the data distortion and on the extension of quantization index modulation (QIM) to circular histograms of numerical attributes. The semantic distortion control of the embedding process we propose relies on the identification of existing semantic links in between values of attributes in a tuple by means of an ontology. By doing so, we avoid incoherent or very rare record occurrences which may bias data interpretation or betray the presence of the watermark. In a second time, we adapt QIM to database watermarking. Watermark embedding is conducted by modulating the relative angular position of the circular histogram center of mass of one numerical attribute. We theoretically demonstrate the robustness performance of our scheme against most common attacks (i.e., tuple insertion and deletion). This makes it suitable for copyright protection, owner identification, or traitor tracing purposes. We further verify experimentally these theoretical limits within the framework of a medical database of more than one half million of inpatient hospital stay records. Under the assumption imposed by the central limit theorem, experimental results fit the theory. We also compare our approach with two efficient schemes so as to prove its benefits.

In this paper, we present the results of a preliminary work which focuses on identifying the system one Digital Radiography (DR) image has been produced by. To do so, we adapt one solution proposed for digital camera devices where the photo response non-uniformity noise of charge coupled device sensors is used as camera fingerprint. In particular, we show that DR acquisition systems leave a similar Digital Radiography Pattern Noise (DRPN) that can be used as fingerprint. In order to extract this DRPN and due to the nature of DR images, we further propose to take advantage of contourlet filtering. Experiments conducted on images issued from 7 different DR systems show first it is possible to identify with good accuracy the origin of one DR image and, second, that contourlet filtering leads to better detection performance than commonly used approaches based on wavelet or BM3D filtering.

In this paper, we adapt the robust reversible watermarking modulation originally proposed by Vleeschouwer for images to the protection of relational databases. The resulting scheme modulates the relative angular position of the circular histogram center of mass of one numerical attribute for message embedding. It can be used for verifying database authentication as well as for traceability when identifying database origin after it has been modified. Beyond the application framework, we theoretically evaluate the performance of our scheme in terms of capacity, distortion, and robustness against two common database modifications: 1) addition and 2) removal of tuples. To that end, we model the impact of the embedding process and of database modifications on the probability distribution of the center of mass position. We further verify experimentally these theoretical limits within the framework of a medical database of more than one million of inpatient hospital stay records. We show that under the assumptions imposed by the central limit theorem, experimental results fit the theory. We also compare our approach with two recent and efficient schemes so as to prove its benefits.

The gyrator transform has been proposed in optics a few years ago. By using the theory of quaternion numbers, this paper presents the quaternion gyrator transform (QGT). It is shown that the QGT can be computed via the left-side type of quaternion Fourier transforms. The new transform is applied to color image encryption for validation, where the rotation angles are used as encryption keys making it more secure compared to a recent method using discrete quaternion Fourier transforms (DQFTs). Experimental results show that the proposed encryption algorithm for color image performs as well as the DQFTs method in terms of noise robustness, so that it could be a useful tool for color image encryption.

In this paper, we propose a new reversible watermarking scheme. One first contribution is a histogram shifting modulation which adaptively takes care of the local specificities of the image content. By applying it to the image prediction-errors and by considering their immediate neighborhood, the scheme we propose inserts data in textured areas where other methods fail to do so. Furthermore, our scheme makes use of a classification process for identifying parts of the image that can be watermarked with the most suited reversible modulation. This classification is based on a reference image derived from the image itself, a prediction of it, which has the property of being invariant to the watermark insertion. In that way, the watermark embedder and extractor remain synchronized for message extraction and image reconstruction. The experiments conducted so far, on some natural images and on medical images from different modalities, show that for capacities smaller than 0.4 bpp, our method can insert more data with lower distortion than any existing schemes. For the same capacity, we achieve a peak signal-to-noise ratio (PSNR) of about 1-2 dB greater than with the scheme of Hwang , the most efficient approach actually.

In this paper, we present a medical image integrity verification system to detect and approximate local malevolent image alterations (e.g., removal or addition of lesions) as well as identifying the nature of a global processing an image may have undergone (e.g., lossy compression, filtering, etc.). The proposed integrity analysis process is based on nonsignificant region watermarking with signatures extracted from different pixel blocks of interest, which are compared with the recomputed ones at the verification stage. A set of three signatures is proposed. The first two devoted to detection and modification location are cryptographic hashes and checksums, while the last one is issued from the image moment theory. In this paper, we first show how geometric moments can be used to approximate any local modification by its nearest generalized 2-D Gaussian. We then demonstrate how ratios between original and recomputed geometric moments can be used as image features in a classifier-based strategy in order to determine the nature of a global image processing. Experimental results considering both local and global modifications in MRI and retina images illustrate the overall performances of our approach. With a pixel block signature of about 200 bit long, it is possible to detect, to roughly localize, and to get an idea about the image tamper.

In case of litigation in telemedicine applications, one main issue is to determine liabilities of each physician involved in the data exchange. To achieve this goal, we propose an efficient watermarking based telemedicine protocol. Its objective is not only to ensure the security of data but also to provide evidence that an exchange took place. We show how watermarking in combination with cryptographic mechanisms and a third party can enforce data and exchange traceability. The efficiency of our solution is demonstrated by means of a security analysis.

In this paper, we propose a joint encryption/water-marking system for the purpose of protecting medical images. This system is based on an approach which combines a substitutive watermarking algorithm, the quantization index modulation, with an encryption algorithm: a stream cipher algorithm (e.g., the RC4) or a block cipher algorithm (e.g., the AES in cipher block chaining (CBC) mode of operation). Our objective is to give access to the outcomes of the image integrity and of its origin even though the image is stored encrypted. If watermarking and encryption are conducted jointly at the protection stage, watermark extraction and decryption can be applied independently. The security analysis of our scheme and experimental results achieved on 8-bit depth ultrasound images as well as on 16-bit encoded positron emission tomography images demonstrate the capability of our system to securely make available security attributes in both spatial and encrypted domains while minimizing image distortion. Furthermore, by making use of the AES block cipher in CBC mode, the proposed system is compliant with or transparent to the DICOM standard.

This work presents the first method of digital blind forensics within the medical imaging field with the objective to detect whether an image has been modified by some processing (e.g. filtering, lossy compression and so on). It compares two image features: the Histogram statistics of Reorganized Block-based Discrete cosine transform coefficients (HRBD), originally proposed for steganalysis purposes, and the Histogram statistics of Reorganized Block-based Tchebichef moments (HRBT). Both features serve as input of a set of SVM classifiers built in order to discriminate tampered images from original ones as well as to identify the nature of the global modification one image may have undergone. Performance evaluation, conducted in application to different medical image modalities, shows that these image features can help, independently or jointly, to blindly distinguish image processing or modifications with a detection rate greater than 70%. They also underline the complementarity of these features.

The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better.

Geometric distortions are generally simple and effective attacks for many watermarking methods. They can make detection and extraction of the embedded watermark difficult or even impossible by destroying the synchronization between the watermark reader and the embedded watermark. In this paper, we propose a new watermarking approach which allows watermark detection and extraction under affine transformation attacks. The novelty of our approach stands on a set of affine invariants we derived from Legendre moments. Watermark embedding and detection are directly performed on this set of invariants. We also show how these moments can be exploited for estimating the geometric distortion parameters in order to permit watermark extraction. Experimental results show that the proposed watermarking scheme is robust to a wide range of attacks: geometric distortion, filtering, compression, and additive noise.

Traditional access control mechanisms prevent illegal access by controlling access right before executing an action; they belong to a class of a priori security solutions and, from this point of view, they have some limitations, like inflexibility in unanticipated circumstances. By contrast, a posteriori mechanisms enforce policies not by preventing unauthorized access, but rather by deterring it. Such access control needs evidence to prove violations. Evidence is derived from one or several log records, which trace each user's actions. Efficiency of violation detection mostly depends on the compliance of log records with the access control policy. In order to develop an efficient method for finding these violations, we propose restructuring log records according to a security policy model. We illustrate our methodology by applying it to the healthcare domain, taking care of the IHE (Integrating the healthcare enterprise) framework, particularly its basic security profile, ATNA (Audit Trail and Node Authentication). This profile defines log records established on the analysis of common health practice scenarios. We analyze and establish how ATNA log records can be refined in order to be integrated into an a posteriori access and usage control process, based on an expressive and contextual security policy like the OrBAC policy.

Processing blurred images is a key problem in many image applications. Existing methods to obtain blur invariants which are invariant with respect to centrally symmetric blur are based on geometric moments or complex moments. In this paper, we propose a new method to construct a set of blur invariants using the orthogonal Legendre moments. Some important properties of Legendre moments for the blurred image are presented and proved. The performance of the proposed descriptors is evaluated with various point-spread functions and different image noises. The comparison of the present approach with previous methods in terms of pattern recognition accuracy is also provided. The experimental results show that the proposed descriptors are more robust to noise and have better discriminative power than the methods based on geometric or complex moments.

To improve medical image sharing in applications such as e-learning or remote diagnosis aid, we propose to make the image more usable by watermarking it with a digest of its associated knowledge. The aim of such a knowledge digest (KD) is for it to be used for retrieving similar images with either the same findings or differential diagnoses. It summarizes the symbolic descriptions of the image, the symbolic descriptions of the findings semiology, and the similarity rules that contribute to balancing the importance of previous descriptors when comparing images. Instead of modifying the image file format by adding some extra header information, watermarking is used to embed the KD in the pixel gray-level values of the corresponding images. When shared through open networks, watermarking also helps to convey reliability proofs (integrity and authenticity) of an image and its KD. The interest of these new image functionalities is illustrated in the updating of the distributed users' databases within the framework of an e-learning application demonstrator of endoscopic semiology.