Tatouage numérique des images dans le domaine des ondelettes basé sur la décomposition en valeurs singulières et l'optimisation multi-objective

Depuis l'extraordinaire révolution technique de l'analogique vers le numérique à la fin du 20ième siècle, les documents numériques sont devenus de plus en plus utilisés à cause de leur diffusion peu coûteuse et extrêmement rapide. Cependant ce passage de l'analogique vers le numérique ne s'est pas fait sans engendrer des inquiétudes en terme des droits d'auteurs. Des personnes non autorisées peuvent s'approprier des documents numériques pour faire des profits au dépends des propriétaires légitimes ayant les droits initiaux, puisque son contenu peut être facilement copié, modifié et distribué sans risque d'être détérioré. Dans cette optique, au début des années 1990, une nouvelle technique a été introduite qui s'inspire principalement de la cryptographie et la stéganographie : elle consiste à inscrire une marque dans un document numérique. Cette technique est nommée le tatouage numérique, en anglais digital watermarking. Cette thèse présente cinq différentes contributions relatives au domaine du tatouage numérique et du traitement d'image. La première contribution est la proposition de deux solutions au problème de la détection positive fausse de la marque constatée dans certains algorithmes de tatouage numérique basés sur la décomposition en valeurs singulières. L'une des solutions est basée sur les fonctions de hachage et l'autre sur le cryptage d'image. La deuxième contribution est la proposition d'un algorithme de cryptage d'image basé sur le principe du cube Rubik. La troisième contribution est la conception d'un algorithme de tatouage numérique basé sur la transformée en ondelettes à base du schéma de lifting (LWT) et la décomposition en valeurs singulières (SVD). Un facteur scalaire unique est utilisé pour contrôler l'intensité de l'insertion de la marque, et permet ainsi de trouver le meilleur compromis entre la robustesse et l'imperceptibilité du tatouage numérique. Cependant, l'utilisation des facteurs scalaires multiples au lieu d'un facteur scalaire unique est plus intéressante [CKLS97]. Toutefois, la détermination des valeurs optimales des facteurs scalaires multiples est un problème très difficile et complexe. Afin de trouver ces valeurs optimales, on a utilisé séparément l'optimisation multi-objective par algorithme génétique (MOGAO) et l'optimisation multi-objective par l'algorithme de colonie de fourmis (MOACO) qui sont considérés comme la quatrième et la cinquième contributions de cette thèse

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field