Image forgery localization is critical in defending against the malicious manipulation of image content, and is attracting increasing attention worldwide. In this paper, we propose a Dual-domain Fusion Swin Transformer U-Net (DFST-UNet) for image forgery localization. DFST-UNet is built on a U-shaped encoder–decoder architecture. Swin Transformer blocks are integrated into the U-Net architecture to capture long-range context information and perceive forged regions at different scales. Considering the fact that high-frequency forgery information is an essential clue for forgery localization, a dual-stream encoder is proposed to comprehensively expose forgery clues in both the RGB domain and the frequency domain. A novel high-frequency feature extractor module (HFEM) is designed to extract robust high-frequency features. A hierarchical attention fusion module (HAFM) is designed to effectively fuse the dual-domain features. Extensive experimental results demonstrate the superiority of DFST-UNet over the state-of-the-art methods in the task of image forgery localization.

A great challenge to steganography has arisen with the wide application of steganalysis methods based on convolutional neural networks (CNNs). To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) has been proposed and achieved success for spatial image steganography. However, the application of GAN to JPEG steganography is still in the prototype stage; its antidetectability and training efficiency should be improved. In conventional steganography, research has shown that the side information calculated from the precover can be used to enhance security. However, it is hard to calculate the side information without the spatial domain image. In this work, an embedding cost learning framework for JPEG image steganography via a GAN (JS–GAN) has been proposed, the learned embedding cost can be further adjusted asymmetrically according to the estimated side information (ESI). Experimental results have demonstrated that the proposed method can automatically learn a content‐adaptive embedding cost function, and using the ESI properly can effectively improve the security performance. For example, under the attack of a classic steganalyzer GFR with a quality factor of 75 and 0.4 bpnzAC, the proposed JS–GAN can increase the detection error by 2.58% over J‐UNIWARD, and the ESI–aided version JS–GAN (ESI) can further increase the security performance by 11.25% over JS–GAN.

With the rapid development of image generation techniques, it becomes much more difficult to distinguish high‐quality computer‐generated (CG) images from photographic (PG) images, challenging the authenticity and credibility of digital images. Therefore, distinguishing CG images from PG images has become an important research problem in image forensics, and it is crucial to develop reliable methods to detect CG images in practical scenarios. In this paper, we proposed a forensics contrastive learning (FCL) framework to adaptively learn intrinsic forensics features for the general and robust detection of CG images. The data augmentation module is specially designed for CG image forensics, which reduces the interference of forensic‐irrelevant information and enhances discrimination features between CG and PG images in both the spatial and frequency domains. Instance‐wise contrastive loss and patch‐wise contrastive loss are simultaneously applied to capture critical discrepancies between CG and PG images from global and local views. Extensive experiments on different public datasets and common postprocessing operations demonstrate that our approach can achieve significantly better generalization and robustness than the state‐of‐the‐art approaches. This manuscript was submitted as a pre‐print in the following link https://papers.ssrn.com/-sol3/papers.cfm?abstract_id=4778441 .