Towards JPEG-Resistant Image Forgery Detection and Localization Via Self-Supervised Domain Adaptation.

With wide applications of image editing tools, forged images (splicing, copy-move, removal and etc.) have been becoming great public concerns. Although existing image forgery localization methods could achieve fairly good results on several public datasets, most of them perform poorly when the forged images are JPEG compressed as they are usually done in social networks. To tackle this issue, in this paper, a self-supervised domain adaptation network, which is composed of a backbone network with Siamese architecture and a compression approximation network (ComNet), is proposed for JPEG-resistant image forgery detection and localization. To improve the performance against JPEG compression, ComNet is customized to approximate the JPEG compression operation through self-supervised learning, generating JPEG-agent images with general JPEG compression characteristics. The backbone network is then trained with domain adaptation strategy to localize the tampering boundary and region, and alleviate the domain shift between uncompressed and JPEG-agent images. Extensive experimental results on several public datasets show that the proposed method outperforms or rivals to other state-of-the-art methods in image forgery detection and localization, especially for JPEG compression with unknown QFs.

Blind Forensics of Successive Geometric Transformations in Digital Images Using Spectral Method: Theory and Applications.

Geometric transformations, such as resizing and rotation, are almost always needed when two or more images are spliced together to create convincing image forgeries. In recent years, researchers have developed many digital forensic techniques to identify these operations. Most previous works in this area focus on the analysis of images that have undergone single geometric transformations, e.g., resizing or rotation. In several recent works, researchers have addressed yet another practical and realistic situation: successive geometric transformations, e.g., repeated resizing, resizing-rotation, rotation-resizing, and repeated rotation. We will also concentrate on this topic in this paper. Specifically, we present an in-depth analysis in the frequency domain of the second-order statistics of the geometrically transformed images. We give an exact formulation of how the parameters of the first and second geometric transformations influence the appearance of periodic artifacts. The expected positions of characteristic resampling peaks are analytically derived. The theory developed here helps to address the gap left by previous works on this topic and is useful for image security and authentication, in particular, the forensics of geometric transformations in digital images. As an application of the developed theory, we present an effective method that allows one to distinguish between the aforementioned four different processing chains. The proposed method can further estimate all the geometric transformation parameters. This may provide useful clues for image forgery detection.

Rate and Distortion Optimization for Reversible Data Hiding Using Multiple Histogram Shifting.

Histogram shifting (HS) embedding as a typical reversible data hiding scheme is widely investigated due to its high quality of stego-image. For HS-based embedding, the selected side information, i.e., peak and zero bins, usually greatly affects the rate and distortion performance of the stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes utilize some empirical criterion to determine those side information, which generally could not lead to a globally optimal solution for reversible embedding. In this paper, based on the developed rate and distortion model, the problem of HS-based multiple embedding is formulated as the one of rate and distortion optimization. Two key propositions are then derived to facilitate the fast computation of distortion due to multiple shifting and narrow down the solution space, respectively. Finally, an evolutionary optimization algorithm, i.e., genetic algorithm is employed to search the nearly optimal zero and peak bins. For a given data payload, the proposed scheme could not only adaptively determine the proper number of peak and zero bin pairs but also their corresponding values for HS-based multiple reversible embedding. Compared with previous approaches, experimental results demonstrate the superiority of the proposed scheme in the terms of embedding capacity and stego-image quality.

Blind detection of median filtering in digital images: a difference domain based approach.

Recently, the median filtering (MF) detector as a forensic tool for the recovery of images' processing history has attracted wide interest. This paper presents a novel method for the blind detection of MF in digital images. Following some strongly indicative analyses in the difference domain of images, we introduce two new feature sets that allow us to distinguish a median-filtered image from an untouched image or average-filtered one. The effectiveness of the proposed features is verified with evidence from exhaustive experiments on a large composite image database. Compared with prior arts, the proposed method achieves significant performance improvement in the case of low resolution and strong JPEG post-compression. In addition, it is demonstrated that our method is more robust against additive noise than other existing MF detectors. With analyses and extensive experimental researches presented in this paper, we hope that the proposed method will add a new tool to the arsenal of forensic analysts.