The RAWtoRGB reconstruction involves rendering a high-quality color image from raw data acquired by an image sensor. Many recent approaches, including deep learning models to their training paradigms, exhibit limitations in terms of algorithm complexity or image quality due to unwanted artifacts. Existing solutions often inadvertently introduce visual distortions that are often overlooked and underrepresented in evaluation metrics. Furthermore, the challenge of balancing hardware portability and image quality is often underrated. This paper therefore introduces the Grid Regularization Enhancement Network (GRENet), a lightweight RAWtoRGB model with only 16.7k parameters. GRENet demonstrates high-end performance, achieving state-of-the-art PSNR scores of small models (20.63 dB on ZRR and 24.76 dB on MAI2021). A key innovation is a novel supervised training loss, termed Grid loss, specifically designed to mitigate checkerboard-alike artifacts. This loss function not only improves reference metrics, with a gain of +0.25 dB on MAI2021 PSNR but also significantly enhances the visual quality of rendered images. To ensure a comprehensive evaluation, we assess GRENet with a wide range of carefully selected no reference image quality metrics along with the full reference ones. In addition and unlike most of the prior work, we conduct an in-depth visual rendering inspection, comparing GRENet’s reconstructions with those of state-of-the-art models. This analysis reveals a taxonomy of image artifacts commonly observed in reconstructions, providing valuable insights into the strengths and limitations of current approaches. Our comparisons demonstrate that GRENet exhibits a high reliability in mitigating visual distortions among existing methods.

Today, the popularity of 3D videos is increasing significantly. This trend can be attributed to their immersive appeal and lifelike experience. In an era dominated by the widespread distribution of digital content, data integrity, and ownership, all of these elements are of crucial importance. In this context, the practice of traitor tracing, closely related to Digital Rights Management (DRM), facilitates the identification and tracking of unauthorized users who have violated copyright in order to share illegal copyright-protected content. In this paper, we propose a solution to this problem, we introduce an innovative traitor tracing approach focused on 3D video, with a particular focus on the DIBR (Depth Image-Based Rendering) format, which can be vulnerable to an Interleaving attack strategy. For this purpose, we develop a new phylogeny tree construction method designed to combat collusion attacks. Our experimental evaluations demonstrate the effectiveness of our proposed approach particularly when applied to long fingerprinting codes. Compared to Tardos’ approach, our method delivers very good results, even for a large number of colluders.

With growing storage and the diffusion of multimedia data across digital networks, protecting visual content is a subject of interest in research, especially via the means of image content obscuration methods. Although several of these techniques have been explored to provide basic to advanced protection against re-identification by humans or automated recognition systems, few achieve full key-based reversibility while introducing minimal distortion to the resulting image. In this paper, we propose a novel image content obscuration method that leverages variational autoencoders. Our approach reduces the dimensionality of images into latent vectors of a source class, and then applies three distinct transformations to the latent representation to match a target class. These transformations are designed to be both visually imperceptible and reversible using a secret key, enabling the original content to be accurately reconstructed. We evaluate our method through qualitative and quantitative experiments regarding the obscuration and defense against re-identification, and compare to previous image obscuration methods.

Recently, thumbnail-preserving encryption (TPE) has attracted significant attention because it maintains certain visual information in encrypted images, thereby achieving a trade-off between visual usability and privacy protection. However, existing TPE schemes utilize independent encryption between pixels, blocks, or channels, which results in excessive robustness and consequently introduces security vulnerabilities. To address this issue, we propose a triple-chain architecture to achieve sensitive TPE. This architecture integrates three interdependent encryption chains at the pixel, block, and channel levels, establishing cryptographic dependencies such that any minor modification to either the original or encrypted image leads to completely divergent encryption/decryption outcomes. Specifically, the architecture introduces three chained encryption mechanisms: (1) pixel-level chained encryption propagates encrypted pixels outputs as the part of subsequent inputs; (2) block-level hashing derives new keys from plaintext-key concatenations; and (3) channel-level dependencies are established via iterative key derivation. This cascading structure ensures cryptographic interdependency across all levels. The experimental results verify that the proposed architecture achieves sensitive TPE for the first time while preserving the TPE characteristics of visual usability and privacy protection.

The growing use of 3D objects in applications such as augmented and virtual reality and computer-aided design poses serious challenges in terms of compression and security, particularly during transmission, storage in the cloud, and deployment in virtual environments such as the Metaverse. 3D crypto-compression, which integrates encryption and compression, provides an efficient solution, particularly joint compression and encryption methods, which ensure data confidentiality and storage efficiency while maintaining format compliance. In this article, we present a new and efficient method of selective crypto-compression using the Draco 3D compression format. Our approach applies selective encryption just after vertex quantization, using Advanced Encryption Standard in Cipher FeedBack mode to preserve random access, and decorrelate vertex positions. We further propose an adaptive selective encryption strategy that dynamically adjusts selective encryption parameters according to the desired visual security level. In addition, we introduce an edge length distribution analysis to evaluate the statistical security of the 3D object crypto-compression method we propose. Experimental results presented on a large database demonstrate that our method achieves an optimal tradeoff between compression efficiency and security, ensuring format compliance.

In multimedia forensics, most research focuses on analyzing individual assets, such as images or videos, to verify their authenticity by analyzing their intrinsic properties. However, provenance analysis examines multiple assets collectively, tracing their histories through pairwise relationships. This approach is beneficial in identifying traces of manipulation and understanding the evolution of digital content. In this research, we apply provenance analysis to detect and track malicious users engaged in collusion attacks by analyzing their fingerprinting codes. Specifically, the correlations we consider are not generic media content similarities, but rather statistical and structural relationships between the embedded fingerprinting codes of different multimedia assets. By quantifying these correlations, our method can reliably identify individuals responsible for generating unauthorized copies and distinguish between different collusion strategies. Furthermore, it allows us to classify and estimate the specific type of collusion attack employed, providing deeper insights into the strategies used by attackers, thereby aiding in the development of more robust countermeasures and enhancing the resilience of multimedia security systems.

The widespread use of 3D objects in many fields poses new challenges for their secure transmission, storage and visualization. While selective encryption methods allow format compliance and enable adjustable security by selectively encrypting 3D geometry, they produce visible noise and disrupt geometric coherence which reduces compression efficiency. In this paper, we propose a reversible deformation method for 3D object protection that breaks with state-of-the-art 3D security approaches by offering more natural and visually coherent protection. The deformation is applied directly to the geometry, controlled by a secret key, and can be adjusted to achieve the desired level of visual security. Experimental results presented on a large database demonstrate that our method offers protection comparable to selective encryption, while providing significantly greater resilience to reconstruction attacks such as Laplacian smoothing, particularly at low security levels where selective encryption is more vulnerable. This work introduces a new direction for format-compliant 3D object protection, designed for secure visualization in untrusted environments.

A recently proposed 3D object encryption scheme enables hierarchical decryption, allowing a single encryption to display varied visual effects upon decryption. This has potential applications in complex scenarios where 3D objects require access at different security levels. However, it has two main issues: lossless decryption is not possible due to precision loss from the IEEE 754 standard, and it is customized for a specific AES algorithm, limiting support for others. Motivated by this, a novel paradigm for 3D object encryption with differentiated visual effects upon decryption is proposed. In this paradigm, the precision loss is preserved within the 3D encrypted object, allowing the 3D object after fully decrypting to be identical to the original, and thus it islossless. Meanwhile, the data to be encrypted is reduced to three blocks, which are generalized bitstreams. Bitstream encryption algorithms that do not produce ciphertext expansion can be applied, and it operates independently of the other components in the paradigm, making ituniversal. Finally, a prototype of this paradigm is constructed, using the same encryption algorithm as in the previous scheme for comparative experiments. Two 3D object datasets are used to conduct the experiments and results demonstrate that it achieves lossless decryption and has a time advantage, while also undergoing security analysis and verification against smoothing attacks to confirm the security.

Generative AI has seen major developments in recent years, enhancing the realism of synthetic images, also known as computer-generated images. In addition, generative AI has also made it possible to modify specific image characteristics through image editing. Previous work has developed methods based on generative adversarial networks (GANs) for generating realistic images, in particular faces, but also to modify specific features. However, this work has never been applied to specific animal species. Moreover, the assessment of the results has been generally done subjectively, rather than quantitatively. In this article, we propose an approach based on methods for generating images of faces of male or female mandrills, a non-human primate. The main novelty of proposed method is the ability to edit their sex by identifying a sex axis in the latent space of a specific GAN. In addition, we have developed an assessment of the sex levels based on statistical features extracted from real image distributions. The experimental results we obtained from a specific database are not only realistic, but also accurate, meeting a need for future work in behavioral experiments with wild mandrills.

The Metaverse is a 3D interactive virtual community that has gained significant attention in academia, business, and industry as a potential future internet paradigm. In this space, avatars serve as key elements, acting as the primary means of human interaction. Avatars are expected to be created using real data, tailored to users' preferences, and controlled in real-time through signals from wearable devices. Avatars allow users to feel as though they are extensions of their own bodies, creating an immersive experience that blurs the line between virtual and real compared to other virtual communities. On the other hand, the avatar faces serious security and privacy problems, especially when people and the law/regulation are increasingly less tolerant of security and privacy, such as copyright, false identity detection, dataset security, authentication, and content tampering. This special issue collects 15 papers reporting the recent developments of security and privacy of avatar in metaverse. For the Avatar Copyright Protection. "A Self-Defense Copyright Protection Scheme for NFT Image Art Based on Information Embedding" addresses copyright issues related to avatars produced in the Metaverse and proposes a copyright protection scheme that not only enables tracking and verification of avatar content transactions but also validates the legality of the source and ownership of the avatar content. "Invisible Adversarial Watermarking: A Novel Security Mechanism for Enhancing Copyright Protection" addresses the potential for unauthorized access and use of image datasets used to generate avatars and proposes an image protection method that combines adversarial perturbations with invisible watermarks. This approach not only prevents illegal use of the image datasets but also enables effective tracking of data copyright. In "FaceDefend: Copyright Protection to Prevent Face Embezzle, " the authors propose a solution to the misuse problem arising from the theft of real facial image data used in avatar generation, based on defensive strategies. This approach effectively ensures copyright protection for real facial data. For the False Identity Detection for Avatars. The authors of "Audio-Visual Contrastive Pre-train for Face Forgery Detection" address the issue of potential facial privacy breaches due to the realism of avatars in virtual worlds, which can lead.

In our present society, 3D objects play an increasingly important role in many different domains, especially with the development of meta environments. Many applications demand large 3D objects, which makes their compression a requirement. Meanwhile, 3D objects are also exposed to various security problems during online storage and transmission. Consequently, security aspects such as copyright and authentication are essential for 3D objects. In this paper, we propose a simultaneous 3D object watermarking and compression method based on Draco. A watermarking step is integrated within Draco, Google’s 3D compression method, which is rapidly being universally adopted as standard. The proposed method enables a large bit embedding rate, which can be used for copyright information. The proposed method is also, to the best of our knowledge, the first watermarking method for 3D objects compressed with Draco.

This paper presents a reliable image and video secret storing method, aiming at ensuring the protected storage of multimedia files without risk for their owner to have their privacy violated. It relies on a particular property (standardized in MPEG-A Part.21 VIMAF) of video and image standards such as H.264/AVC, H.265/HEVC and MPEG-HEIF: a non negligible portion of the bitstream (so-called cipherable bits) can be altered without modifying the decodability of the stream. Such a modification results in an image or a video that is both a valid and visually encrypted media file. Not willing to be bothered by a complex key management system, we take inspiration from Shamir's secret sharing scheme, and propose a method where the secret to be shared is the original (unciphered) set of cipherable bits, that are encoded and divided in$n$shares, among which$k-1$or less do not permit to reconstruct the secret, while$k$or more allow to recover it. Those$n$shares are then used to generate$n$ciphered fully standard compliant versions of the media. The rightful owner of the file will easily store these$n$versions in various Cloud storage locations, and recover them all when the fully deciphered file will be needed, while a hacker will most generally obtain only some of those files, hence not be capable to decrypt the file.

Nowadays, 3D objects are frequently stored and shared online, where they become vulnerable to attacks. Therefore, applying security measures such as encryption is crucial. Once the 3D object is encrypted and stored online, a third party who is not authorized to access the content of the 3D object may need to embed hidden data in the 3D object. In this paper, we propose a new MSB-based reversible data-hiding in the encrypted domain method for 3D objects, which uses a Hamiltonian path to establish a unique order for the vertices of a 3D object. For the reconstruction step, we select the smallest distance between vertices to accurately predict the MSB value of the mantissa for each coordinate. Our proposed method is format compliant, avoids any size expansion, and does not require auxiliary file, while guaranteeing the reversibility of the marked encrypted 3D object, even when tested on a large database of 3D objects.

Generations of scientists have pursued the goal of defining beauty. While early scientists initially focused on objective criteria of beauty ('feature-based aesthetics'), philosophers and artists alike have since proposed that beauty arises from the interaction between the object and the individual who perceives it. The aesthetic theory of fluency formalizes this idea of interaction by proposing that beauty is determined by the efficiency of information processing in the perceiver's brain ('processing-based aesthetics'), and that efficient processing induces a positive aesthetic experience. The theory is supported by numerous psychological results, however, to date there is no quantitative predictive model to test it on a large scale. In this work, we propose to leverage the capacity of deep convolutional neural networks (DCNN) to model the processing of information in the brain by studying the link between beauty and neuronal sparsity, a measure of information processing efficiency. Whether analyzing pictures of faces, figurative or abstract art paintings, neuronal sparsity explains up to 28% of variance in beauty scores, and up to 47% when combined with a feature-based metric. However, we also found that sparsity is either positively or negatively correlated with beauty across the multiple layers of the DCNN. Our quantitative model stresses the importance of considering how information is processed, in addition to the content of that information, when predicting beauty, but also suggests an unexpectedly complex relationship between fluency and beauty.

With the rise of exchanges over the cloud and social networks, JPEG images have taken an important place in world image transmission and storage. In order to avoid security breaches and combat threats on the Internet, numerous JPEG image security methods have been proposed in both the academic and industrial communities. Encryption methods have been specifically designed to make JPEG images visually secure using so called crypto-compression methods. The drawback of crypto-compression is that it depends on only one secret key. Indeed, if this key is lost, so is the totality of the content of the secret original image. In this paper, we propose a secret JPEG image sharing approach. Shamir’s secret sharing scheme over Galois fields is used during the JPEG Huffman coding step, ensuring the visual security of the secret image in the compressed domain, while solving the issue of secret key loss. In addition, we also describe an eco-friendly scenario, dealing with a public shared JPEG image. In this scenario, we can obtain compressed shares because there is no need to duplicate the redundant information. According to our obtained results, our approach is fully format compliant and size preserving when compared to a standard JPEG compression of a secret original image, while ensuring the visual security of the content of the secret original image.

With the increasing popularity of 3D objects in industry and everyday life, 3D object security has become essential. While there exists methods for 3D selective encryption, where a clear 3D object is encrypted so that the result has the desired level of visual security, to our knowledge, no method exists for decrypting encrypted 3D objects hierarchically. In this paper, we are the first to propose propose a method which allows us to hierarchically decrypt an encrypted 3D object according to a generated ring of keys. This ring consists of a set of keys that allow a stronger or weaker decryption of the encrypted 3D object. Each hierarchically decrypted 3D object has a different visual security level, where the 3D object is more or less visually accessible. Based on a master key, these hierarchical keys are generated using our method during the encryption process. Our method is essential when it comes to preventing trade secrets from being leaked from within a company or by exterior attackers. It is also ecologically friendly and more secure than traditional selective encryption methods.

For over a decade, 3D objects are an increasingly popular form of media. It has become necessary and urgent to secure them during their transmission or archiving. In this article, we propose a new method to obtain a watermarked 3D object from high-capacity data hiding in the encrypted domain. Based on the homomorphic properties of the Paillier cryptosystem, our proposed method allows us to embed several secret messages in the encrypted domain with a high-capacity. These messages can be extracted in the plain-text domain after the 3D object decryption. To the best of our knowledge, we are the first to propose a data hiding method in the encrypted domain where the high-capacity watermark is conserved in the plain-text domain after the 3D object is decrypted. The encryption and the data hiding in the encrypted domain are format compliant and without size expansion, despite the use of the Paillier cryptosystem. Each time a new message is embedded in the encrypted domain, flags are added in order to indicate which blocks are still available for the embedding of additional messages. After the decryption of a watermarked encrypted 3D object, our method produces a watermarked 3D object which is visually very similar to the original 3D object. From the decrypted watermarked 3D object, we can then extract all the embedded messages directly in the plain-text domain, without the need for an auxiliary file. Moreover, large keys are used, rending our method secure for real-life applications.

In this paper, new models of chaotic attractors are presented. In doing so, an old chaotic attractor based on the mathematical model of a Variable Structure Model of Neurons (VSMN) is developed. It is then generated by neuron with oscillators. As a result, new styles of new chaotic attractors are found. Also, a detailed bifurcation analysis of those new chaotic systems with theory and simulations is discussed. Finally, the numerical examples will illustrate each model with its simulation using Python. We found out that the new systems are chaotic and could be used for different applications like image encryption. Bifurcation, Chaotic attractor, neuron, oscillators, Variable Structure Model of Neurons.

Along with the development of cloud computing, ensuring fast and secure image exchanges became necessary. In this context, the scientific community has taken a growing interest in JPEG image security. Crypto-compression is generally used to make secure the content of a secret image, while allowing to reduce its size. The drawback of crypto-compression is that it depends on a secret key. Indeed, if this key is lost, so is the content of the secret image. In this paper, we propose a joint secret image sharing and JPEG compression method. Shamir’s secret sharing scheme is used during the JPEG Huffman coding step, ensuring the visual security of the secret image in the compressed domain, while solving the problem of secret key loss. Our approach is fully format compliant and size preserving when compared to a standard JPEG compression of the original secret image.

3D objects play an essential role in industry and everyday life. They are often stored on the cloud and transferred many times during their existence, and so 3D object security is essential. To the best of our knowledge, no method exists to hierarchically decrypt fully encrypted 3D objects. In this paper, we propose a new method to hierarchically decrypt a 3D object so that the visual accessibility of the resulting 3D object depends on the user's rights. For that, it is based on the hierarchy of the key used during the decryption. The decryption key is selected from a ring of hierarchical keys which are generated and distributed to the respective users during the encryption process. The proposed method is eco-friendly as a single 3D object is fully encrypted (confidential level) and available to several users with different rights. Experimental results show that our method is efficient and can be used for real applications.

Today, 3D objects are an increasingly popular form of media. It has become necessary to secure them during their transmission or archiving. In this paper, we propose a two tier reversible data hiding method for 3D objects in the encrypted domain. Based on the homomorphic properties of the Paillier cryptosystem, our proposed method embeds a first tier message in the encrypted domain which can be extracted in either the encrypted domain or the clear domain. Indeed, our method produces a marked 3D object which is visually very similar to the original object. It seeks to be format compliant and to preserve the original size of the data, without the need for an auxiliary file. Moreover, large keys are used, rending our method secure for real life applications.

Autonomous vehicles are able to sense their environment and operate without human involvement. In particular, they have to be able to locate themselves. In order to achieve this task, geolocalisation files such as RINEX files are used. Due to the large amount of data, these files are very large and there is a big challenge in reducing their size for real-time navigation. However, this problem has not really been explored for RINEX files. In this paper, we propose an efficient method to losslessly compress RINEX files based on quadratic polynomial interpolation for autonomous vehicles navigation. Indeed, due to the orbits of the satellites around the earth, satellite signals can be modeled by parabolic curves. After interpolation of this model, prediction errors are computed as the differences between each original value of the satellite signal and its associated predicted value. Finally, these prediction errors are compressed using entropy coding. Experimental results show that our proposed method allows us to outperform the compression rate obtained by a previous state-of-the-art approach.

During the last few decades, the transmission of images over secure networks has exponentially grown. Data security in certain applications such as secure storage, authentication or privacy protection on cloud platforms, require specific strategies for multimedia. Cryptography can be used for this purpose. Indeed, using a secret key, it is possible to make data unreadable in order to secure it. Although encryption approaches are effective to make the original data unreadable, they are also very sensitive to noise. Because of the introduction of noise into an encrypted image during its transmission or storage, the original data cannot be recovered. In this article, we first describe a new encryption mode called CFB-then-ECB and based on a combination of the CFB mode and the ECB mode for AES encryption. Using this new encryption mode, if one encrypted pixel block is noised, this will result in two incorrectly reconstructed pixel blocks during the decryption (the current and the following pixel blocks). This noise spreading is then exploited in a new proposed approach of noisy encrypted image correction. It contains two main steps involving a classifier to discriminate clear and encrypted pixel blocks. After a direct decryption of a noisy encrypted image, the first step is to identify and localize the pixel blocks that are probably incorrectly decrypted. The second step of our proposed approach is to analyze and correct these pixel blocks. Experimental results show that the proposed method can be used to blindly correct noisy encrypted images, while preserving the image structure without increasing the original data size with additional information.

Prior to the publication of our article in 2018, to our knowledge, there were no methods of achieving a favourable trade-off between the payload in bits-per-pixel (bpp) and the quality of the reconstructed image in terms of PSNR or SSIM. Indeed, a high payload value would lead to a degradation of the reconstructed image’s quality. Moreover, it should also be noted that almost all of the other state-of-the-art methods at the time, were based on Least Significant Bit (LSB) substitution and made little use of the redundancy between pixels in the clear domain to realize the data embedding of a secret message. In our proposed work[2], we have taken the opposing view by developing a Most Significant Bits (MSB) prediction-based reversible data hiding in encrypted images (RDHEI) method. In the EPE-HCRDH approach, the original image is encrypted without modification and information about the location of all pixels which cannot be correctly predicted is embedded by MSB substitution. In order to localize the prediction errors, flags of consecutive bits equal to 1 are used. With this information, the data hider can detect all the bits which can be marked and substitute them with bits of a secret message. In this case, the payload is slightly lower than 1 bpp, but perfect reversibility is achieved. So, the proposed EPE-HCRDH approach provides a high payload with a little complexity. But as highlighted by Dragoi and Coltuc[1], the fact of using flags, so that the data hider can embed a secret message introduces security flaws in the method. Despite this, the method has attracted the attention of many researchers, with 100 citations (according to Google Scholar on November 9, 2020) in several peer-reviewed journals of excellent reputation (IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY[3], IEEE TRANSACTIONS ON MULTIMEDIA[4]–[7], IEEE ACCESS[8], IEEE TRANSACTIONS ON SIGNAL PROCESSING, and IEEE TRANSACTION ON DEPENDABLE AND SECURE COMPUTING[9]. Today, we can say that high capacity RDHEI has become a hot topic.

Reversible data hiding in encrypted images (RDHEI) can be used as an effective technique to embed additional data directly in the encrypted domain and therefore, without any invasion to privacy. In this way, RDHEI is especially useful for labeling encrypted images in cloud storage. In this paper, we propose a new method of data hiding in encrypted images, which is fully reversible and has a very high payload. All the bit-planes of an image are processed recursively, from the most significant one to the least significant by combining error prediction, reversible adaptation, encryption and embedding. For pixel prediction, the Median Edge Detector, also called LOCO-I and known to be efficient in JPEG-LS compression standard, is used for each bit-plane. Moreover, conversely to current state-of-the-art methods, in our proposed method, there is no pre-processing step to correct incorrectly predicted pixels and no flags to highlight them. Indeed, a reversible adaptation of the bit-planes is performed in order to make it possible to detect and correct all incorrectly predicted pixels during the decoding step. Thanks to the high correlation between pixels in the clear domain, a large part of the bits of an image can be substituted by bits of a secret message. Our experiments show that we can generally embed bits of the secret message until the fourth most-significant bit-plane of an image, this allows us to have an average payload value of 2.4586 bpp.

The rising popularity of social networks and cloud computing has greatly increased a number of JPEG compressed image exchanges. In this context, the security of the transmission channel and/or the cloud storage can be susceptible to privacy leaks. The selective encryption is an efficient tool to mask the image content and to protect confidentiality while remaining format-compliant. However, image processing in the encrypted domain is not a trivial task. In this paper, we present a JPEG crypto-compression method which allows us to recompress a JPEG crypto-compressed image several times, without any information about the secret key or the original image content. Indeed, using the proposed method in this paper, each recompression can be done directly on the JPEG bitstream by removing the last bit of the code representation of each non-zero coefficient, adapting the entropic code part, and slightly modifying the quantization table. This method is efficient to recompress JPEG crypto-compressed images in terms of compression ratio. Moreover, the decryption of the recompressed image produces an image with a very similar visual quality when compared to the original image, according to the obtained results.

New issues have arisen in the creation of 3D objects linked to collaboration in 3D workflows. In this kind of usage it may be necessary to allow access and to share a lower quality file of a 3D object for some collaborators. In this paper, we present a Format-Compliant Selective Secret 3D Object Sharing (FCSS3DOS) scheme, which visually protects the content using geometrical distortions by selectively sharing bits of a 3D object's geometry using Shamir's Secret Sharing scheme. The degradation level D is selected before the sharing process and determines how much geometrical distortions are induced into n generated shared 3D objects which are given to n participants. When at least k or more participants agree to combine their own shared 3D objects, then the secret 3D object can be recovered without distortion. The value of k can vary between 2 and n. In experimental results, we ana-lyze the degradation level D that influences the geometrical distortions induced into shared 3D objects.

Social network development raises many issues relating to privacy protection for images. In particular, multi-party privacy protection conflicts can take place when an image is published by only one of its owners. Indeed, privacy settings applied to this image are those of its owner and people on the image are not involved in the process. In this paper, we propose a new hierarchical secret image sharing scheme for social networks in order to answer this problem. Based on the disjunctive multi-level approach of Belenkiy applied to images, this solution ensures user privacy, as shown by our obtained experimental results.

In this research project, we are interested by finding solutions to the problem of image analysis and processing in the encrypted domain. For security reasons, more and more digital data are transferred or stored in the encrypted domain. However, during the transmission or the archiving of encrypted images, it is often necessary to analyze or process them, without knowing the original content or the secret key used during the encryption phase. We propose to work on this problem, by associating theoretical aspects with numerous applications. Our main contributions concern: data hiding in encrypted images, correction of noisy encrypted images, recompression of crypto-compressed images and secret image sharing.

Direct observation to study biodiversity can be time consuming, however, other methods often provide indirect measurements and are possibly biased. To solve these problems, images can be a useful tool and ecologists have started to rely more and more on images as a source of data and on automated image analysis. However, the existing methods mostly perform image classification. In this paper we present an efficient method based on object detection to access deeper information the content of an image. Using high resolution images, we built a pipeline to slice the original images, perform detections and later refine these observations. We illustrate the interest of this pipeline by using it on-field images taken in agroforestery banana-coffee systems to study invertebrate communities around the banana pests Cosmopolites sodidus and Metamasius sp. and the interactions between the different animals within this community. Experimental results show that our pipeline reaches 87.8% F1-score and allows us to successfully detect and identify 23 species and ant castes. These 23 species are divided into 7 super-classes, but the ant super-class, that shows more individuals and interactions is described more precisely. We are then able to study the interaction network between different species of this community and identify major predators of banana pests within this ecosystem.

This paper presents a new method of secret three-dimensional object sharing (S3DOS), which allows sharing of three-dimensional (3-D) objects, while preserving its file format by selectively encrypting a 3-D object in order to sufficiently protect the visual nature of the content. This scheme, named format-compliant selective (k, n) S3DOS, modifies selected bits of the vertices of a 3-D object to protect the visual content by inducing geometrical distortions. These distortions are controlled thanks to the application of a degradation level at the beginning of the sharing process. To reconstruct the secret 3-D object, at least k shared 3-D objects among n have to be combined in order to remove the geometrical distortions and recover the exact original 3-D object. Experimental results are presented and evaluated to showcase the feasibility of the proposed scheme.

The authentication of printed documents is an important problem these days. Numerous authentication techniques have been proposed in the relevant literature. One of the most promising solutions uses copy-sensitive graphical codes made of particular patterns. The two Level QR (2LQR) code uses specific textured patterns in order to ensure the sensitivity to duplication process. The authentication test of this code is based on comparing the correlation values between the original and the printed-and-scanned codes with a pre-determined threshold. The weakest feature of this technique is the rejection of authentic codes due to the small gap between correlation values for authentic and duplicated code. In this paper, we propose to reduce the number of false-negative results of the authentication test by using a very competitive Super-Resolution (SR) technique. The experimental results show the significant improvement of correlation values when using the images printed-and-scanned once, without increasing the correlation values of duplicated codes. Therefore, the 2LQR code copy sensitivity is not affected by the suggested quality improvement process.

Images that have been altered and more specifically spliced together have invaded the digital domain due to the ease with which we are able to copy and paste them. To detect such forgeries the digital image processing community is proposing new automatic algorithms designed to help human operators reveal manipulated images. In this paper, we focus on a local detection system, which considers which tampered areas produce local statistical effects that do not impact neighboring areas or the image as a whole. We propose to study how the definition of local blocks, considering their size and overlap, impacts final pixel detection. We also propose new features which are an original way to consider the noise of an image as a colored signal. Indeed, in a non-forged image, there is a high correlation of noise between the three color channels R, G and B. We show that an optimal configuration can be defined and in this case the proposed approach outperforms several previously proposed methods using the same tested dataset, in uncompressed and JPEG modes. Note, in this paper we only focus on feature extraction without using machine learning.

Reversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryption key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. In the last few years, RDHEI has started to draw research interest. Indeed, with the development of cloud computing, data privacy has become a real issue. However, none of the existing methods allow us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. We present two approaches, these are: high capacity reversible data hiding approach with correction of prediction errors and high capacity reversible data hiding approach with embedded prediction errors. With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity.

3D steganography is used in order to embed or hide information into 3D objects without causing visible or machine detectable modifications. In this paper we rethink about a high capacity 3D steganography based on the Hamiltonian path quantization, and increase its resistance to steganalysis. We analyze the parameters that may influence the distortion of a 3D shape as well as the resistance of the steganography to 3D steganalysis. According to the experimental results, the proposed high capacity 3D steganographic method has an increased resistance to steganalysis.

For security reasons, more and more digital data are transferred or stored in encrypted domains. In particular for images, selective format-compliant JPEG encryption methods have been proposed for the last ten years. Since encryption is selective, in order to reduce the processing time and to be format-compliant, it is now necessary to evaluate the confidentiality of these selective crypto-compressed JPEG images. It is known that image quality metrics, such as PSNR or SSIM, give a very low correlation with a mean opinion score (MOS) for low quality images. In this paper, we propose an efficient confidentiality metric based on the visual saliency diffusion. We show experimentally that this metric is well correlated with a MOS and efficient to evaluate the confidentiality of selective crypto-compressed JPEG images.

Visual Secret Sharing (VSS) is a type of cryptographic method used to secure digital media such as images by splitting it into n shares. Then, with k or more shares, the secret media can be reconstructed. Without the required number of shares, they are totally useless individually. The purpose of secret sharing methods is to reinforce the cryptographic approach from different points of failure as a single information-container. Instead of Lagrange's interpolation employed by classical methods, an approach based on linear equations and coding theory resolves the classic problems of VSS methods like pixel expansion and information losses at recovery time. In this paper, we propose an efficient (2, n) secret image sharing scheme without loss of information and size increase of shares, with n ≤ 7 based on linear equations encoded into eight bits to encode separately pixels and permutations to maintain high entropy in shares.

This paper presents a compression scheme for 3D dynamic models. 3D affine transforms are used to compute the prediction errors. In order to improve the coding efficiency, the prediction error quantization is optimized using a rate control mechanism.

The availability of high quality copy machines provides a large amount of printed document counterfeits. Numerous authentication techniques based on security printing, graphical codes, hashing or local hashing have been suggested earlier. In this paper, we propose a novel printed document authentication system based on sensitivity of a new two level QR (2LQR) code to copying process. This 2LQR code contains specific textured patterns, which are sensitive to printing and copying processes. Therefore, it can be used to detect unauthorized document duplication. Experimental results show the efficiency of this 2LQR code for copy detection.

In multimedia distribution platforms, one of the main challenges is to provide an efficient and accurate tracing process despite the lack of information about the colluders' strategy. Indeed, the original Tardos tracing performance is considered as suboptimal because of its agnostic behavior and conservative accusation regardless the collusion strategy. The Expectation Maximization algorithm has shown to be an efficient solution to estimate the collusion channel and thus to tune the Tardos accusation functions. In this paper, we explore the impact of this algorithm in a group-based tracing scheme to deal with the computational costs and the invariance of the Tardos accusation performance. The tracing scheme we propose benefits from a twofold accusation process. Indeed, in a first time, it is based on a two-level tracing strategy which consists in tracing guilty groups in a first level with the Boneh Shaw tracing code and in retrieving at least one colluder in accused groups with Tardos code in the second level. This strategy has reduced efficiently the decoding process of the Tardos code. The main shift we propose in the second level is to apply the Expectation Maximization algorithm to be tightly tied to collusion yielded by colluders and hence to find the more accurate Tardos accusation functions. The performance of the resulting tracing scheme is evaluated according to different criteria and promising results have been achieved when compared to the existing tracing schemes proposed in the literature.

The quick response (QR) code was designed for storage information and high-speed reading applications. In this paper, we present a new rich QR code that has two storage levels and can be used for document authentication. This new rich QR code, named two-level QR code, has public and private storage levels. The public level is the same as the standard QR code storage level; therefore, it is readable by any classical QR code application. The private level is constructed by replacing the black modules by specific textured patterns. It consists of information encoded using q-ary code with an error correction capacity. This allows us not only to increase the storage capacity of the QR code, but also to distinguish the original document from a copy. This authentication is due to the sensitivity of the used patterns to the print-and-scan (P&S) process. The pattern recognition method that we use to read the second-level information can be used both in a private message sharing and in an authentication scenario. It is based on maximizing the correlation values between P&S degraded patterns and reference patterns. The storage capacity can be significantly improved by increasing the code alphabet q or by increasing the textured pattern size. The experimental results show a perfect restoration of private information. It also highlights the possibility of using this new rich QR code for document authentication.

3D meshes are widely used today in very different domains for example; game, medical diagnostic, CAD (computed aided design) or more recently 3-D printing. In this paper, we provide a new data hiding method that has a huge capacity, cp = 3c(n - 1), where n is the vertex number of the mesh and c is an integer. The proposed method consists to compute a Hamiltonian path along the mesh as synchronization. At each step of path building, 3c bits are embedded. The embedding is designed to be a distance relation between a vertex and its father in the path. Moreover, the method uses static arithmetic coding to embed message information. We analyzed the proposed method by inserting RGB images in 3D meshes.

This study presents a new local feature matching approach that relies upon Reeb graph (RG)‐based representation as well as a simple and accurate similarity estimation. The central contribution of this work is to reinforce the topological consistency conditions of the graph‐based description. Formally, the RGs are enriched with geometry signatures based on parameterisation approaches. After RG construction, the shape is segmented into Reeb charts of controlled topology mapped to its canonical planar domain. Then, two stretching signatures, corresponding to the area and angle distortion, are determined and taken as three‐dimensional‐shape descriptor. The similarity estimation is performed in two steps. The first one consists in forming the pairs of similar Reeb charts, according to the minimal distance between their corresponding signatures. The second step is to measure the global similarity which quantifies the similitude degree between all the matched Reeb charts. Retrieval experiments conducted on four publicly available databases have shown that the proposed matching scheme yields satisfactory results. Among observations, it can be noticed that despite its rapidity, the method provides an overall retrieval efficiency gain compared to very recent state‐of‐the‐art methods.

This paper presents a new retrieval approach for 3D non-rigid objects. The proposed method consists in using the Reeb graph representation as local shape descriptor. The generated Reeb graph, based on the heat diffusion properties, is segmented into Reeb charts having a controlled topology. Each Reeb chart is associated with a couple of geometrical signatures, based on the area and angle distortions. The matching procedure is carried out on each pair of Reeb charts, according to the minimum distance between the corresponding signatures. A global similarity measure quantifies the degree of correspondence between all the matched Reeb charts. Experimental results, conducted on SHREC 2011 dataset, have shown that our retrieval approach provides an overall retrieval efficiency gain compared to three state-of-the-art methods.

3-D object security is increasingly brought to the attention of the public by the expansion of new multimedia technologies such as the 3-D printing. In the development of crypto-security systems of 3-D objects, we can identify two major directions represented by the cryptography and digital watermarking. A good security system has to be format compliant, has to preserve the original bit rate and, whenever possible, it should be reversible. Watermarking methodology has the advantage of ensuring that the embedded hidden message can be verified at any processing stage such as the transmission, storage and when visualizing the embedding media. In this paper, we review the previous work in 3-D security and analyze the crypto-security of a 3-D watermarking method which embeds information by mesh surface distortion minimization. Then, we discuss future avenues of research by presenting emerging applications.

In this paper, we investigate finite mixture models (FMM) and adaptive arithmetic coding (AAC) for block-based lossless image compression. The AAC performance depends on how well the model fits the source symbols' statistics. In addition, when encoding small block, the number of source symbols is considerably large by comparison with the number of samples in that block, which results in a loss of compression efficiency. To this end, we propose to model each block with an appropriately FMM by maximizing the probability of samples that belong to that block. The mixture parameters are estimated through maximum likelihood using the Expectation-Maximization (EM) algorithm in order to maximize the arithmetic coding efficiency. The comparative studies of some particular test images prove the efficiency of the mixture models for lossless image compression. The experimental results show significant improvements over conventional adaptive arithmetic encoders and the state-of-the-art lossless image compression standards and algorithms.

This paper presents a new approach of Reeb graph extraction adapted to 3D dynamic triangular Meshes. Particularly, we propose a new continuous scalar function, used for Reeb graph construction. This function is based on the heat diffusion properties. The restriction of the heat kernel to temporal domain makes the scalar function intrinsic and stable against perturbations. Due to the presence of neighborhood information in the heat kernel associated to each vertex, the proposed Reeb Graph extraction can be extremely useful as local shape descriptor for non-rigid shape retrieval. Experiments show that the proposed structural analysis technique achieves high accuracy and stability under topology changes and various perturbations through time.

In this study, the authors proposed a new scheme which performs both lossless compression and encryption of images. Lossless compression is done by arithmetic coding (AC) while encryption is based on a chaos‐based pseudorandom bit generator. Hence, they proposed to incorporate recent results of chaos theory into AC in order to shuffle the cumulative frequency vector of input symbols chaotically to make AC secure and the decoding process completely key‐dependent. Many other techniques based on varying the statistical model used by AC have been proposed in literature, however, these techniques suffer from losses in compression efficiency that result from changes in entropy model statistics and are weak against known attacks. The proposed compression–encryption techniques were developed and discussed. The numerical simulation analysis indicates that the proposed scheme is highly satisfactory for image encryption without any AC compression efficiency loss. In addition, it can be incorporated into any image compression standard or algorithm employing AC as entropy coding stage, including static, adaptive and context‐based adaptive models, and at any level, including bit, pixel and predictive error pixel levels.

This paper presents one of the first methods allowing the protection of the newly emerging video codec HEVC (High Efficiency Video Coding). Visual protection is achieved through selective encryption (SE) of HEVC-CABAC binstrings in a format compliant manner. The SE approach developed for HEVC is different from that of H.264/AVC in several aspects. Truncated rice code is introduced for binarization of quantized transform coefficients (QTCs) instead of truncated unary code. The encryption space (ES) of binstrings of truncated rice codes is not always dyadic and cannot be represented by an integer number of bits. Hence they cannot be concatenated together to create plaintext for the CFB (Cipher Feedback) mode of AES, which is a self-synchronizing stream cipher for so-called AES-CFB. Another challenge for SE in HEVC concerns the introduction of context, which is adaptive to QTC. This work presents a thorough investigation of HEVC-CABAC from an encryption standpoint. An algorithm is devised for conversion of non-dyadic ES to dyadic, which can be concatenated to form plaintext for AES-CFB. For selectively encrypted binstrings, the context of truncated rice code for binarization of future syntax elements is guaranteed to remain unchanged. Hence the encrypted bitstream is format-compliant and has exactly the same bit-rate. The proposed technique requires very little processing power and is ideal for playback on hand held devices. The proposed scheme is acceptable for DRM of a wide range of applications, since it protects the contour and motion information, along with texture. Several benchmark video sequences of different resolutions and diverse contents were used for experimental evaluation of the proposed algorithm. A detailed security analysis of the proposed scheme verified the validity of the proposed encryption scheme for content protection in a wide range of applications.

Nowadays, there is an increasing number of digital comics, so-called e-comics, available on Internet. Protecting these ecomics without Digital Rights Management (DRM) is a new issue. In this paper, we propose a method to ensure the protection of e-comics by encrypting only bubbles so that the story will not be understandable. To develop this method, we used standards like JPEG compression and AES encryption. The first step of the method involves detecting bubbles, and we decided to use the MSER algorithm, which is the most commonly used for text detection. The second main step involves making these areas unreadable by encrypting AC coefficients of the JPEG block, corresponding to high frequencies during entropy coding. Several experimental results show the efficiency of the proposed method.

HUGO [1] and MOD [2] are the most secure adaptive embedding algorithms of 2011. These algorithms strive to hide a secret message, while minimizing an ad hoc embedding impact. They use a detectability map, which, if properly defined, is correlated to the security. In this paper, we present a new adaptive embedding scheme: Adaptive Steganography by Oracle (ASO). It is based on an oracle used to calculate the detectability map, and this oracle use the Kodovský's ensemble classifiers [3]. Our approach preserves both cover image and sender's database distributions during the embedding process, which improves the security. In addition, it offers to the sender the opportunity to choose the most reliable image( s), during his secret communication. Experimental results show that our embedding scheme presents good security performances, since the detection error of ASO is much higher than that of HUGO.

With the ever-increasing development of digital technologies and digital 3D models, the question of 3D mesh protection has becoming more and more important. One of the problems in digital watermarking is to decide how to embed in a 3D mesh as many bits of information as possible while ensuring that the hidden information can be correctly retrieved after 3D mesh compression. This paper describes a hybrid watermarking/compression system, adapted to semi-regular 3D meshes. The central contribution is to integrate blind watermarking, to geometric progressive compression algorithm. The latter consists on semi-regular wavelet transform and zerotree coding approach. The proposed embedding method operates in the discrete wavelet transform domain using dither modulation principal. This work shows the ability of joint watermarking/compression scheme to guarantee watermark robustness against compression attacks while inducing a low quality degradation.

This paper presents a new decoder for probabilistic binary traitor tracing codes under the marking assumption. It is based on a binary hypothesis testing rule which integrates a collusion channel relaxation so as to obtain numerical and simple accusation functions. This decoder is blind as no estimation of the collusion channel prior to the accusation is required. Experimentations show that using the proposed decoder gives better performance than the well-known symmetric version of the Tardos decoder for common attack channels.

Efficient data synchronization for 3D mesh models is a new major challenge. 3D models are also now being used to an increasing extent and with current network technology, 3D model compression, visualization and protection demand increases. However, there is still no perfect method to achieve this data synchronization while being blind and robust against various attacks. We propose a blind method based on a Hamiltonian path to synchronize vertex clouds, and particularly on 3D meshes. We experimentally show that it is fast and robust against Gaussian noise. This novel approach can generate better results than mesh-based methods because it does not use vertex connectivity.

In the field of video protection, selective encryption (SE) is a scheme which ensures a visual security of video by encrypting a small part of data. This paper presents a new SE algorithm for H.264/AVC in CAVLC mode. This algorithm controls the amount of encrypted alternative coefficients (AC) of the integer transform in the entropic encoder. Two visual quality measures, the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM), are used to measure the visual confidentiality level of each video frame and to regulate the amount of encrypted alternatives coefficients. This method can be applied on intra and inter frame video sequences.

In an increasing number of image and video processing problems, cryptographic techniques are used to enforce content access control, identity verification and authentication, and privacy protection. The combination of cryptography and signal processing is an exciting emerging field. This introductory paper gives an overview of approaches and challenges that exist in applying cryptographic primitives to important image and video processing problems, including (partial) content encryption, secure face recognition, and secure biometrics. This paper aims to help the community in appreciating the utility and challenges of cryptographic techniques in image and video processing.

In this paper, a novel data hiding strategy is proposed to integrate disparity data, needed for 3D visualization based on depth image based rendering, into a single H.264 format video bitstream. The proposed method has the potential of being imperceptible and efficient in terms of rate distortion trade-off. Depth information has been embedded in some of quantized transformed coefficients (QTCs) while taking into account the reconstruction loop. This provides us with a high payload for embedding of depth information in the texture data, with a negligible decrease in PSNR. To maintain synchronization, the embedding is carried out while taking into account the correspondence of video frames. Three different benchmark video sequences containing different combinations of motion, texture and objects are used for experimental evaluation of the proposed algorithm.

This paper presents a contribution in matter of robustness of a previous method of 3D objects watermarking [10] based on wavelet decomposition on semi-regular triangular meshes. This decomposition gives rise to a multiresolution mesh where the watermark could be embedded at each level of resolution, in this case we talk about a hierarchical watermarking. In practice the robustness of the previous method against geometrical attacks was high only in the low resolution which makes it far from being a hierarchical method. Our analysis made on the fragility of that method allowed us to reveal the cause which was not primarily the bad bits quantization of the watermark, but rather the alteration of the sequencing of the embedded bits (synchronization). Our contribution is to replace the previous geometrical mechanism with a new topological synchronization mechanism, which has resulted a real hierarchical and robust watermarking.

This paper presents a novel method for the protection of bitstreams of state-of-the-art video codec H.264/AVC. The problem of selective encryption (SE) is addressed along with the compression in the entropy coding modules. H.264/AVC supports two types of entropy coding modules. Context-adaptive variable length coding (CAVLC) is supported in H.264/AVC baseline profile and context-adaptive binary arithmetic coding (CABAC) is supported in H.264/AVC main profile. SE is performed in both types of entropy coding modules of this video codec. For this purpose, in this paper the encryption step is done simultaneously with the entropy coding CAVLC or CABAC. SE is performed by using the advanced encryption standard (AES) algorithm with the cipher feedback mode on a subset of codewords/binstrings. For CAVLC, SE is performed on equal length codewords from a specific variable length coding table. In case of CABAC, it is done on equal length binstrings. In our scheme, entropy coding module serves the purpose of encryption cipher without affecting the coding efficiency of H.264/AVC by keeping exactly the same bitrate, generating completely compliant bitstream and utilizing negligible computational power. Owing to no escalation in bitrate, our encryption algorithm is better suited for real-time multimedia streaming over heterogeneous networks. It is perfect for playback on handheld devices because of negligible increase in processing power. Nine different benchmark video sequences containing different combinations of motion, texture, and objects are used for experimental evaluation of the proposed algorithm.

In this paper, we present a novel approach for active finger-printing of state of the art video codec H.264/AVC. Tardos probabilistic fingerprinting code is embedded in H.264/AVC video signals using spread spectrum watermarking technique. Different linear and non-linear collusion attacks have been performed in the pixel domain to show the robustness of the proposed approach. The embedding has been performed in the non-zero quantized transformed coefficients (QTCs) while taking into account the reconstruction loop.

In this paper, a novel method for fast protection of AVS video coding standard alongwith compression is presented. Here the problems of compression and selective encryption (SE) have been simultaneously addressed for AVS part-2 Jizhun profile. It is performed in the context-based 2D variable length coding (C2DVLC) module of video codec. SE is performed by using the Advanced Encryption Standard (AES) algorithm with the Cipher Feedback (CFB) mode on a subset of codewords. C2DVLC serves the purpose of encryption step without affecting the coding efficiency of AVS by keeping the bitrate unchanged, generating completely compliant bitstream and utilizing negligible computational power. Nine different benchmark video sequences containing different combinations of motion, texture and objects are used for experimental evaluation of the proposed algorithm.

An adaptive spread spectrum synchronous scalable (A4S) data hiding strategy is being put forward to integrate the disparate 3D facial visualization data, into a single JPEG2000 format file with the aim to cater diverse clients in various bandwidth scenarios. The method is both robust and imperceptible in the sense that the robustness of the spread spectrum (SS) is coupled with the removable embedding that ensures highest possible visualization quality. The SS embedding of the DWT-domain 2.5D facial model is carried out in the transform domain YCrCb components, of the 2D texture, from the coding stream of JPEG2000 codec just after the DWT stage. High depth map quality is ensured through the adaptation of synchronization during embedding that would exclude some highest frequency subbands. The results show that the method can be exploited for video-surveillance and video-conference applications.

This paper presents a new selective and scalable encryption (SSE) method for intra dyadic scalable coding framework based on wavelet/subband (DWTSB) for H.264/AVC. It has been achieved through the scrambling of quantized transform coefficients (QTCs) in all the subbands of DWTSB. To make the encryption scalable, it takes advantage of the prior knowledge of the frequencies which are dominant in different high frequency (HF) subbands, as traditional zigzag scan is not that efficient for them. Thus, by scrambling the scan order of QTCs in the intra scalable coding framework of H.264/AVC, we were able to get encryption and compression for enhancement layers (ELs) simultaneously. Watermarking has been integrated in the proposed architecture to avoid the requirement of separate keys for each spatial layer. The algorithm is better suited for multimedia streaming as the bitrate of the encrypted bitstream is lower than the original bitrate. Besides offering SSE, the proposed algorithm, when applied to different benchmark video sequences, outperformed the standard zigzag scan in terms of bitrate.

This paper presents a novel method for the protection of copyrighted multimedia content. The problem of selective encryption (SE) has been addressed along with compression for the state of the art video codec H.264/AVC. SE is performed in the context-based adaptive binary arithmetic coding (CABAC) module of video codec. For this purpose, CABAC is converted to an encryption cipher by scrambling of equal length binarized code words. In our scheme, CABAC engine serves the purpose of encryption cipher without affecting the coding efficiency of H.264/AVC by keeping the original bitrate, generating completely compliant bitstream and utilizing negligible computational cost. Nine different benchmark video sequences containing different combinations of motion, texture and objects have been subjected to experimental evaluation of the proposed algorithm.

Full-waveform (FW) lidar systems provide range profiles of the Earth topography. They are acquired from airborne platforms or from satellites. Many applications derive from the use of such data, from the extraction of 3D point clouds to the inversion of vegetation profiles. Nevertheless, handling range profiles is much more difficult than handling 3D point cloud. The aim of this paper is to present a research tool based on opensource libraries that can process and visualize such data. We focused our work on the implementation on the 2D/3D interface that gives the possibility to visualize the interaction between the lidar electromagnetic waves and the Earth topography. Moreover, this tool integrates several processing steps of FW Lidar data.

In this paper, we present a novel attack named colorization attack. This attack is specific to color-hiding watermarking schemes. The objective of this work is to demonstrate the feasibility of such an attack and thus to take it into account for the future color-hiding watermarking schemes.

A method for 3D scalable visualization, in a client/server environment is presented. The main idea presented in this paper is to increase the quality of 3D visualization for low bit rate transmission. All informations like texture, digital elevation model and projection systems are merged into a single file. The integration is achieved via data hiding whereas the scalability is realized through the multiresolution nature of JPEG2000 encoding. The embedding step is done in the lossless DWT domain. The strategy is flexible and it is up to the user to decide the level of transform of texture and DEM. In this context a comparison between various possibilities is presented by applying the method to a practical example. It is shown that a very good visualization can be realized with even a tiny fraction of the encoded coefficients.

In this paper a new method is presented for 3-D terrain visualization via reversible JPEG2000-based blind data hiding with special focus on data synchronization and scalability. Online real-time 3-D terrain visualization involves considerable amount of data. The process is essentially the mapping of the aerial photograph, called texture, onto its corresponding digital elevation model (DEM) implying at least two distinct data inputs. The presence of large disparate data necessitates a compression strategy on one hand and the integration of the DEM and texture into one unit on the other. Whilst the compression must accommodate the scalability requirement originated by the diversity of clients, the unification of data ought to be synchronous. For scalability this paper relies on the multiresolution nature of the DWT-based JPEG2000 standard whereas the synchronized unification of DEM with the texture is realized by the application of a perceptually transparent data hiding strategy in the DWT domain. The proposed method is blind in the sense that only a secret key, if any, and the size of the original DEM are needed to extract the data from the texture image. We believe that this is one of the pioneering methods to propose scalable embedding of DEM in the texture image. The method is cost effective, in terms of memory and bandwidths, which is an advantage, especially, in real-time environments when quicker transfer of data is required. The results of a 3-D visualization simulation effected with our method were encouraging and gave a useful insight to the effectiveness of our method in various bandwidth scenarios.

In this paper, we propose a method to embed the color information of an image in its corresponding grey-level image. The objective of this work is to allow free access to the grey-level image and give color image access to secret key owners. This method is made of two steps which are the color image decomposition (in a grey-level image and its associated color information) and the data-hiding. The main contribution of this paper is the energetic function proposed to model the decomposition of the color image. The optimization of the proposed energetic function leads to the obtention of an index image and a color palette. The good properties of that decomposition are an index image which is similar to the luminance of the color image and a color palette which is well suit for the data-hiding. The obtained results confirm the model quality.

In this paper, we propose a method to embed the color information of an image in a corresponding grey-level image. The objective of this work is to allow free access to the grey-level image and give color image access only if you own a secret key. This method is made of three major steps which are a fast color quantization, an optimized ordering and an adapted data hiding. The principle is to build an index image which is, in the same time, a semantically intelligible grey-level image. In order to obtain this particular index image, which should be robust to data hiding, a layer running algorithm is proceeded to sort the K colors of the palette. The major contributions of this paper are the fast color quantization, the optimized layer running algorithm, the color palette compression and the adapted data hiding.

This paper deals with face tracking in difficult conditions of non calibrated camera, strong head motions, thanks to a deformable 3D model. In those conditions, the proposed approach is able to detect and track a face. The novelty is mainly due to a multi-resolution Active Model search which allows to catch strong head motions. Results show an improvement between the single and the multi-resolution technique. Near real-time results are also provided.

In this study we propose a new approach for selective encryption in the Huffman coding of the Discrete Cosine Transform (DCT) coefficients using the Advanced Encryption Standard (AES). The objective is to partially encrypt the human face in an image or video sequence. This approach is based on the AES stream ciphering using Variable Length Coding (VLC) of the Huffman's vector. The proposed scheme allows the decryption of a specific region of the image and results in a significant reduction in encrypting and decrypting processing time. It also provides a constant bit rate while maintaining the JPEG and MPEG bitstream compliance.

In this paper a region-based color image watermarking algorithm is presented. The objective of the method is to embed a particular message in each region of interest (ROI) in the image. The embedding message has to be detected after image manipulations such as cropping, rotations and color JPEG compression. By using shape information, the embedding message is synchronized with each ROI on each color component Y, Cr and Cb. To embed information non-rounded DCT coefficients are watermarked. Experimental results demonstrate the performance of the algorithm against spatial and frequential attacks.

A set of monocular images of a curved painting is taken from different viewpoints around its curved surface. After deriving the surface localization in the camera coordinate system we backproject the image on the curved surface and we flatten it. We analyze the perspective distortions of the scene in the case when it is mapped on a cylindrical surface. Based on the result of this analysis we derive the necessary number of views in order to represent the entire scene depicted on a cylindrical surface. We employ a matching-based mosaicing method for reconstructing the scene from the curved surface. The proposed method is appropriate to be used for painting reconstruction.

The paper presents an approach for reconstructing images painted on curved surfaces. A set of monocular images is taken from different viewpoints in order to mosaic and represent the entire scene. By using a priori knowledge about the support surface of the picture, we derive the surface localization in the camera coordinate system. An automatic mosaicing method is applied on the patterned images in order to obtain the complete scene. The mosaiced scene is visualized on a new synthetic surface by a mapping procedure.