François Cayre

Blind and Robust Watermarking of 3D Models: How to Withstand the Cropping Attack?

State-of-the-art blind and robust 3D watermarking schemes already withstand combinations of a wide variety of attacks (e.g. noise addition, simplification, smoothing, ...) except cropping. This attack is however very common and should be dealt with in a copyright protection framework. In this paper, we propose a technique which enables to extend the robustness of such schemes to cropping. Our algorithm proceeds by the automatic detection of robust shape feature points which are then used for the embedding of a watermark in a local neighborhood. We show that robustness against cropping and other common attacks is achieved provided that at least one feature point as well as its corresponding local neighborhood are retrieved.

JPEG Anti-Forensics With Improved Tradeoff Between Forensic Undetectability and Image Quality

This paper proposes a JPEG anti-forensic method, which aims at removing from a given image the footprints left by JPEG compression, in both the spatial domain and DCT domain. With reasonable loss of image quality, the proposed method can defeat existing forensic detectors that attempt to identify traces of the image JPEG compression history or JPEG anti-forensic processing. In our framework, first because of a total variation-based deblocking operation, the partly recovered DCT information is thereafter used to build an adaptive local dithering signal model, which is able to bring the DCT histogram of the processed image close to that of the original one. Then, a perceptual DCT histogram smoothing is carried out by solving a simplified assignment problem, where the cost function is established as the total perceptual quality loss due to the DCT coefficient modification. The second-round deblocking and de-calibration operations successfully bring the image statistics that are used by the JPEG forensic detectors to the normal status. Experimental results show that the proposed method outperforms the state-of-the-art methods in a better tradeoff between the JPEG forensic undetectability and the visual quality of processed images. Moreover, the application of the proposed anti-forensic method in disguising double JPEG compression artifacts is proven to be feasible by experiments.

A variational approach to JPEG anti-forensics

The objective of JPEG anti-forensics is to remove all the possible footprints left by JPEG compression. By contrary, there exist detectors that attempt to identify any telltale of the image tampering operation of JPEG compression and JPEG anti-forensic processing. This paper makes contribution on improving the undetectability of JPEG anti-forensics, with a higher visual quality of processed images. The employment of constrained total variation based minimization for deblocking successfully fools the forensic methods detecting JPEG blocking, and another advanced JPEG forensic detector. Calibration-based detector is also defeated by conducting a further feature value optimization. Experimental results show that the proposed method outperforms the state-of-the-art methods in a better trade-off between forensic undetectability and visual quality of processed images.

Optimal Transport for Secure Spread-Spectrum Watermarking of Still Images

This paper studies the impact of secure watermark embedding in digital images by proposing a practical implementation of secure spread-spectrum watermarking using distortion optimization. Because strong security properties (key-security and subspace-security) can be achieved using natural watermarking (NW) since this particular embedding lets the distribution of the host and watermarked signals unchanged, we use elements of transportation theory to minimize the global distortion. Next, we apply this new modulation, called transportation NW (TNW), to design a secure watermarking scheme for grayscale images. The TNW uses a multiresolution image decomposition combined with a multiplicative embedding which is taken into account at the distribution level. We show that the distortion solely relies on the variance of the wavelet subbands used during the embedding. In order to maximize a target robustness after JPEG compression, we select different combinations of subbands offering the lowest Bit Error Rates for a target PSNR ranging from 35 to 55 dB and we propose an algorithm to select them. The use of transportation theory also provides an average PSNR gain of 3.6 dB on PSNR with respect to the previous embedding for a set of 2000 images.

Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution

Median filtering enjoys its popularity as a widely adopted image denoising and smoothing tool. It is also used by anti-forensic researchers in helping disguise traces of other image processing operations, e.g., image resampling and JPEG compression. This paper proposes an image variational deconvolution framework for both quality enhancement and anti-forensics of median filtered (MF) images. The proposed optimization-based framework consists of a convolution term, a fidelity term with respect to the MF image, and a prior term. The first term is for the approximation of the median filtering process, using a convolution kernel. The second fidelity term keeps the processed image to some extent still close to the MF image, retaining some denoising or other image processing artifact hiding effects. Using the generalized Gaussian as the distribution model, the last image prior term regularizes the pixel value derivative of the obtained image so that its distribution resembles the original one. Our method can serve as an MF image quality enhancement technique, whose efficacy is validated by experiments conducted on MF images which have been previously “salt & pepper” noised. Using another parameter setting and with an additional pixel value perturbation procedure, the proposed method outperforms the state-of-the-art median filtering anti-forensics, with a better forensic undetectability against existing detectors as well as a higher visual quality of the processed image. Furthermore, the feasibility of concealing image resampling traces and JPEG blocking artifacts is demonstrated by experiments, using the proposed median filtering anti-forensic method.

General-Purpose Image Forensics Using Patch Likelihood under Image Statistical Models

This paper proposes a new, conceptually simple and effective forensic method to address both the generality and the fine-grained tampering localization problems of image forensics. Corresponding to each kind of image operation, a rich GMM (Gaussian Mixture Model) is learned as the image statistical model for small image patches. Thereafter, the binary classification problem, whether a given image block has been previously processed, can be solved by comparing the average patch log-likelihood values calculated on overlapping image patches under different GMMs of original and processed images. With comparisons to a powerful steganalytic feature, experimental results demonstrate the efficiency of the proposed method, for multiple image operations, on whole images and small blocks.

On security threats for robust perceptual hashing

Perceptual hashing has to deal with the constraints of robustness, accuracy and security. After modeling the process of hash extraction and the properties involved in this process, two different security threats are studied, namely the disclosure of the secret feature space and the tampering of the hash. Two different approaches for performing robust hashing are presented: Random-Based Hash (RBH) where the security is achieved using a random projection matrix and Content-Based Hash (CBH) were the security relies on the difficulty to tamper the hash. As for digital watermarking, different security setups are also devised: the Batch Hash Attack, the Group Hash Attack, the Unique Hash Attack and the Sensitivity Attack. A theoretical analysis of the information leakage in the context of Random-Based Hash is proposed. Finally, practical attacks are presented: (1) Minor Component Analysis is used to estimate the secret projection of Random-Based Hashes and (2) Salient point tampering is used to tamper the hash of Content-Based Hashes systems.

JPEG anti-forensics using non-parametric DCT quantization noise estimation and natural image statistics

This paper proposes an anti-forensic method that disguises the footprints left by JPEG compression, whose objective is to fool existing JPEG forensic detectors while keeping a high visual quality of the processed image. First we examine the reliability of existing detectors and point out the potential vulnerability of the quantization table estimation based detector. Then we construct a new, non-parametric method to DCT histogram smoothing without any histogram statistical model. Finally JPEG forensic detectors are fooled by optimizing an objective function considering both the anti-forensic terms and a natural image statistical model. We show that compared to the state-of-the-art methods the proposed JPEG anti-forensic method is able to achieve a higher image visual quality while being undetectable under existing detectors.

Towards a realistic channel model for security analysis of authentication using graphical codes

Graphical codes resemble very much the well-known QR-codes, although they are used in a very different application scenario: authenticating and tracing physical goods on which they are printed with small dimensions so as to become uncloneable. In this paper, we propose a security analysis of such codes based on a realistic channel model. We show that the security level of such codes heavily depends on the printing resolution. For the first time, our experimental setup enables to assess the maximum number of graphical codes to be safely printed until a security breach occur in the authentication system.

Practical performance analysis of secure modulations for woa spread-spectrum based image watermarking

This paper presents the first practical analysis of secure modulations for watermarking of still images in the case of a WOA (Watermarked Only Attack) attack framework (the attacker observes only marked contents). Two recent spread spectrum modulations, namely Natural Watermarking (NW) and Circular Watermarking (CW) are compared against classical modulations, namely Spread Spectrum (SS) and Improved Spread Spectrum (ISS). Results are discussed from the distorsion point of view, as well as from the the robustness and security point of view. We emphasize that the experiments were carried out on a rather significant number of images (2000) and demonstrate the relevance of these modulations in a real-world application.