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.

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 theSlackinstant messaging application. We 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.

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.

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.

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.

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.

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.

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.

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.