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International audience; This chapter explains the basics of cryptography, while a description of image encryption. It provides a survey of the state of the art in Joint Photographic Experts Group (JPEG) crypto-compression, designed as a solution to this problem. The chapter illustrates the challenge of image processing in the crypto-compressed domain, notably with respect to one of the first proposed methods, involving the recompression of crypto-compressed images. A secret key is used as the initialization seed for a cryptographically secure pseudo-random number generator to generate a pseudo-random binary sequence, called the encryption stream, of the same size as the message to be encrypted. Image encryption methods must verify two properties: format conformity and size preservation. The chapter presents a state of the art of the broad categories of crypto-compression methods that are compatible with JPEG format, with examples selected on the basis of performance and diversity.

During the last few decades, multimedia security over the cloud has become a major issue. Public-key homomorphism is an efficient approach for data hiding in encrypted images (DHEI). An original image is encrypted using a public key and sent across a network and then processed to embed a secret message directly in the encrypted domain. During the decoding step, a private key is used to obtain a marked reconstructed image, where the secret message can be extracted. In this paper, we propose an efficient method of DHEI based on the Paillier cryptosystem. Using its homomorphic properties, pixel blocks and bits of the message are multiplied in the encrypted domain resulting in an addition in the clear domain. By applying a pre-processing step on the original image before encryption, this addition becomes a least significant bits (LSB) substitution. Experimental results show that using our proposed scheme, we obtain a high payload value ( $1~bpp$ ) without expanding a lot the original image size contrary to current state-of-the-art methods. Indeed, whatever the key size, the expansion rate is always equal to 2. In addition, the original image and the marked reconstructed image are very similar.

International audience; This chapter presents the challenges of the data hiding methods, as well as the constraints linked to the structure of three-dimensional (3D) meshes, in particular its impact on the synchronization step. It describes a recent development, focused on a variety of different approaches. The chapter discusses the current situation and future areas of research in the field of 3D watermarking. It provides the preliminary notions concerning the aspects of watermarking in general, and the digital representation of 3D objects. Robust watermarking in a transformed domain requires a reversible preprocessing step that converts the meshes into coefficients in frequency space. Data hiding can be used as a secret communication tool. The chapter explains the possible improvements to be made in a 3D data hiding process.

International audience; 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 paper, 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.

Avec le développement de l'informatique en nuage, de nombreux utilisateurs téléchargent leurs données multimédia sur des serveurs distants. Pour assurer leur sécurité, ces données sont généralement chiffrées avant leur transmission et leur stockage. Dans ce chapitre, nous nous intéressons à la problématique du traitement de ces données multimédia chiffrées, et plus particulièrement à l'insertion de données cachées dans le domaine chiffré.

Les maillages 3D sont largement utilisés dans l'industrie dans des domaines très différents tels que le jeu vidéo, le diagnostic médical, la CAO ou l’impression 3D. Dans ce contexte, les techniques d'insertion de données cachées sont intéressantes pour dissimuler et préserver des informations associées à un maillage ou à un nuage de points 3D. Dans ce chapitre, nous présentons les enjeux de ces technologies et les contraintes liées à la structure des maillages 3D.

Dans ce chapitre, nous présentons le concept de partage de secret et ses applications aux contenus multimédia comme les images ou plus récemment étudiés les objets 3D. Le partage de secret permet à un ensemble de participants de partager des informations de manière sécurisée entre eux et d’autoriser la récupération de ces informations lorsqu’un sous-ensemble des participants l’autorise.

Today, it has become very easy to manipulate digital images using image processing tools and software such as Adobe Photoshop ( https://www.adobe.com/products/photoshop.html ). Tampering with images by splicing is an operation that consists of cutting-and-pasting an area of an image into another host image. In this paper, we propose to detect and localize such manipulations by analyzing the correlation of the image noise across the three color channels RGB, which is an intrinsic feature of the digital photography acquisition process. More precisely, we propose to detect the border between the background (host image) and the spliced area. Using a sliding window, we detect the blocks that span across the two areas which are characterized by two different color noise correlations. To do this, we propose specific features that are able to highlight these blocks. After the feature extraction, we introduce a learning phase using a Random Forest Classifier. Experimental results, specifically on the Columbia database, show very good results in comparison to other current state of the art methods.