An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images

International audienceReversible 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 encryp-tion 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

Errorless Robust JPEG Steganography using Outputs of JPEG Coders

Robust steganography is a technique of hiding secret messages in images so that the message can be recovered after additional image processing. One of the most popular processing operations is JPEG recompression. Unfortunately, most of today's steganographic methods addressing this issue only provide a probabilistic guarantee of recovering the secret and are consequently not errorless. That is unacceptable since even a single unexpected change can make the whole message unreadable if it is encrypted. We propose to create a robust set of DCT coefficients by inspecting their behavior during recompression, which requires access to the targeted JPEG compressor. This is done by dividing the DCT coefficients into 64 non-overlapping lattices because one embedding change can potentially affect many other coefficients from the same DCT block during recompression. The robustness is then combined with standard steganographic costs creating a lattice embedding scheme robust against JPEG recompression. Through experiments, we show that the size of the robust set and the scheme's security depends on the ordering of lattices during embedding. We verify the validity of the proposed method with three typical JPEG compressors and benchmark its security for various embedding payloads, three different ways of ordering the lattices, and a range of Quality Factors. Finally, this method is errorless by construction, meaning the embedded message will always be readable.Comment: 10 pages, 11 figures, 1 table, submitted to IEEE Transactions on Dependable and Secure Computin

Image processing and analysis in the encrypted domain

Durant cette dernière décennie, la sécurité des données multimédia, telles que les images, les vidéos et les données 3D, est devenue un problème majeur incontournable. Avec le développement d’Internet, de plus en plus d’images sont transmises sur les réseaux et stockées sur le cloud. Ces données visuelles sont généralement à caractère personnel ou peuvent avoir une valeur marchande. Ainsi, des outils informatiques permettant d’assurer leur sécurité ont été développés.Le but du chiffrement est de garantir la confidentialité visuelle des images en rendant aléatoire leur contenu. Par ailleurs, pendant la transmission ou l'archivage des images chiffrées, il est souvent nécessaire de les analyser ou de les traiter sans connaître leur contenu original, ni la clé secrète utilisée pendant la phase de chiffrement. Ce sujet de thèse propose de se pencher sur cette problématique. En effet, de nombreuses applications existent telles que le partage d’images secrètes, l'insertion de données cachées dans des images chiffrées, l’indexation et la recherche d’images dans des bases de données chiffrées, la recompression d'images crypto-compressées, ou encore la correction d’images chiffrées bruitées.Dans un premier axe de recherche, nous présentons tout d’abord une nouvelle méthode d’insertion de données cachées haute capacité dans le domaine chiffré. Dans la plupart des approches de l’état-de-l’art, les valeurs des bits de poids faible sont remplacées pour réaliser l’insertion d’un message secret. Nous prenons ces approches à contre-pied en proposant de prédire les bits de poids fort. Ainsi, une charge utile nettement supérieure est obtenue, tout en conservant une haute qualité de l’image reconstruite. Par la suite, nous montrons qu’il est en effet possible de traiter récursivement tous les plans binaires d’une image pour réaliser l’insertion de données cachées dans le domaine chiffré.Dans un second axe de recherche, nous expliquons comment exploiter des mesures statistiques (entropie de Shannon et réseau neuronal convolutif) dans des blocs de pixels de petite taille (i.e. avec peu d’échantillons) pour différencier un bloc en clair d’un bloc chiffré dans une image. Nous utilisons alors cette analyse dans une application à la correction d’images chiffrées bruitées.Enfin, le troisième axe de recherche développé dans ces travaux de thèse porte sur la recompression d’images crypto-compressées. Dans le domaine clair, les images JPEG peuvent être recompressées avant leur transmission sur des réseaux bas débit, mais l’opération est bien plus complexe dans le domaine chiffré. Nous proposons alors une méthode de recompression des images JPEG crypto-compressées directement dans le domaine chiffré et sans connaître la clé secrète, en s’appuyant sur un décalage binaire des coefficients réorganisés.During the last decade, the security of multimedia data, such as images, videos and 3D data, has become a major issue. With the development of the Internet, more and more images are transmitted over networks and stored in the cloud. This visual data is usually personal or may have a market value. Thus, computer tools have been developed to ensure their security.The purpose of encryption is to guarantee the visual confidentiality of images by making their content random. Moreover, during the transmission or archiving of encrypted images, it is often necessary to analyse or process them without knowing their original content or the secret key used during the encryption phase. This PhD thesis proposes to address this issue. Indeed, many applications exist such as secret images sharing, data hiding in encrypted images, images indexing and retrieval in encrypted databases, recompression of crypto-compressed images, or correction of noisy encrypted images.In a first line of research, we present a new method of high-capacity data hiding in encrypted images. In most state-of-the-art approaches, the values of the least significant bits are replaced to achieve the embedding of a secret message. We take the opposing view of these approaches by proposing to predict the most significant bits. Thus, a significantly higher payload is obtained, while maintaining a high quality of the reconstructed image. Subsequently, we showed that it was possible to recursively process all bit planes of an image to achieve data hiding in the encrypted domain.In a second line of research, we explain how to exploit statistical measures (Shannon entropy and convolutional neural network) in small pixel blocks (i.e. with few samples) to discriminate a clear pixel block from an encrypted pixel block in an image. We then use this analysis in an application to correct noisy encrypted images.Finally, the third line of research developed in this thesis concerns the recompression of crypto-compressed images. In the clear domain, JPEG images can be recompressed before transmission over low-speed networks, but the operation is much more complex in the encrypted domain. We then proposed a method for recompressing crypto-compressed JPEG images directly in the encrypted domain and without knowing the secret key, using a bit shift of the reorganized coefficients

Analyse et traitement des images dans le domaine chiffré

During the last decade, the security of multimedia data, such as images, videos and 3D data, has become a major issue. With the development of the Internet, more and more images are transmitted over networks and stored in the cloud. This visual data is usually personal or may have a market value. Thus, computer tools have been developed to ensure their security.The purpose of encryption is to guarantee the visual confidentiality of images by making their content random. Moreover, during the transmission or archiving of encrypted images, it is often necessary to analyse or process them without knowing their original content or the secret key used during the encryption phase. This PhD thesis proposes to address this issue. Indeed, many applications exist such as secret images sharing, data hiding in encrypted images, images indexing and retrieval in encrypted databases, recompression of crypto-compressed images, or correction of noisy encrypted images.In a first line of research, we present a new method of high-capacity data hiding in encrypted images. In most state-of-the-art approaches, the values of the least significant bits are replaced to achieve the embedding of a secret message. We take the opposing view of these approaches by proposing to predict the most significant bits. Thus, a significantly higher payload is obtained, while maintaining a high quality of the reconstructed image. Subsequently, we showed that it was possible to recursively process all bit planes of an image to achieve data hiding in the encrypted domain.In a second line of research, we explain how to exploit statistical measures (Shannon entropy and convolutional neural network) in small pixel blocks (i.e. with few samples) to discriminate a clear pixel block from an encrypted pixel block in an image. We then use this analysis in an application to correct noisy encrypted images.Finally, the third line of research developed in this thesis concerns the recompression of crypto-compressed images. In the clear domain, JPEG images can be recompressed before transmission over low-speed networks, but the operation is much more complex in the encrypted domain. We then proposed a method for recompressing crypto-compressed JPEG images directly in the encrypted domain and without knowing the secret key, using a bit shift of the reorganized coefficients.Durant cette dernière décennie, la sécurité des données multimédia, telles que les images, les vidéos et les données 3D, est devenue un problème majeur incontournable. Avec le développement d’Internet, de plus en plus d’images sont transmises sur les réseaux et stockées sur le cloud. Ces données visuelles sont généralement à caractère personnel ou peuvent avoir une valeur marchande. Ainsi, des outils informatiques permettant d’assurer leur sécurité ont été développés.Le but du chiffrement est de garantir la confidentialité visuelle des images en rendant aléatoire leur contenu. Par ailleurs, pendant la transmission ou l'archivage des images chiffrées, il est souvent nécessaire de les analyser ou de les traiter sans connaître leur contenu original, ni la clé secrète utilisée pendant la phase de chiffrement. Ce sujet de thèse propose de se pencher sur cette problématique. En effet, de nombreuses applications existent telles que le partage d’images secrètes, l'insertion de données cachées dans des images chiffrées, l’indexation et la recherche d’images dans des bases de données chiffrées, la recompression d'images crypto-compressées, ou encore la correction d’images chiffrées bruitées.Dans un premier axe de recherche, nous présentons tout d’abord une nouvelle méthode d’insertion de données cachées haute capacité dans le domaine chiffré. Dans la plupart des approches de l’état-de-l’art, les valeurs des bits de poids faible sont remplacées pour réaliser l’insertion d’un message secret. Nous prenons ces approches à contre-pied en proposant de prédire les bits de poids fort. Ainsi, une charge utile nettement supérieure est obtenue, tout en conservant une haute qualité de l’image reconstruite. Par la suite, nous montrons qu’il est en effet possible de traiter récursivement tous les plans binaires d’une image pour réaliser l’insertion de données cachées dans le domaine chiffré.Dans un second axe de recherche, nous expliquons comment exploiter des mesures statistiques (entropie de Shannon et réseau neuronal convolutif) dans des blocs de pixels de petite taille (i.e. avec peu d’échantillons) pour différencier un bloc en clair d’un bloc chiffré dans une image. Nous utilisons alors cette analyse dans une application à la correction d’images chiffrées bruitées.Enfin, le troisième axe de recherche développé dans ces travaux de thèse porte sur la recompression d’images crypto-compressées. Dans le domaine clair, les images JPEG peuvent être recompressées avant leur transmission sur des réseaux bas débit, mais l’opération est bien plus complexe dans le domaine chiffré. Nous proposons alors une méthode de recompression des images JPEG crypto-compressées directement dans le domaine chiffré et sans connaître la clé secrète, en s’appuyant sur un décalage binaire des coefficients réorganisés

High-Capacity Reversible Data Hiding in Encrypted Images using MSB Prediction

International audienceWith the development of cloud computing, data privacy has become a major problem. Reversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the en-crypted domain. Indeed, a lot of methods have been proposed, but none allows a large amount of embedding capacity with a perfect reversibility. In this work, we present a new method of reversible data hiding in encrypted images using MSB (most significant bit) prediction. In order to reconstruct the original image without any errors during the decryption phase, we adapt the to-be-inserted message. Some of the pixels' MSB values are used to highlight the prediction errors and the remaining values are replaced by bits of the secret message. Results show that it is still possible to embed a large message (payload close to 1 bpp)

A Recursive Reversible Data Hiding in Encrypted Images Method With a Very High Payload

International audienceReversible 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 stateof-the-art methods, in our proposed method, there is no preprocessing 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 mostsignificant bit-plane of an image, this allows us to have an average payload value of 2.4586 bpp. Index Terms-Image security, image encryption, reversible data hiding, recursive process, bit-plane prediction, signal processing in the encrypted domain

CFB-then-ECB Mode-Based Image Encryption for an Efficient Correction of Noisy Encrypted Images

International audienceDuring 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

Noisy Encrypted Image Correction based on Shannon Entropy Measurement in Pixel Blocks of Very Small Size

International audienceMany techniques have been presented to protect image content confidentiality. The owner of an image encrypts it using a key and transmits the encrypted image across a network. If the recipient is authorized to access the original content of the image, he can reconstruct it losslessly. However, if during the transmission the encrypted image is noised, some parts of the image can not be deciphered. In order to localize and correct these errors, we propose an approach based on the local Shannon entropy measurement. We first analyze this measure as a function of the block-size. We provide then a full description of our blind error localization and removal process. Experimental results show that the proposed approach, based on local entropy, can be used in practice to correct noisy encrypted images, even with blocks of very small size

Reversible data hiding in encrypted images based on adaptive local entropy analysis

International audienceWith the development of cloud computing, the growth in information technology has led to serious security issues. For this reason, a lot of multimedia files are stored in encrypted forms. Methods of reversible data hiding in encrypted images (RDHEI) have been designed to provide authentication and integrity in the encrypted domain. The original image is firstly encrypted to ensure confidentiality, by making the content unreadable. A secret message is then embedded in the encrypted image, without the need of the encryption key or any access to the clear content. The challenge lies in finding the best trade-off between embedding capacity and quality of the reconstructed image. In 2008, Puech et al. suggested using the AES algorithm to encrypt an original image and to embed one bit in each block of 16 pixels (payload = 0.0625 bpp) [12]. During the decryption phase, the original image is reconstructed by measuring the standard deviation into each block. In this paper, we propose an improvement to this method, by performing an adaptive local entropy measurement. We can achieve a larger payload without altering the recovered image quality. Our obtained results are very good and better than most of the modern state-of-the-art methods, whilst offering an improved security level with the use of the AES algorithm, defined as the encryption standard by the NIST