“Physical” watermarking, i.e., print-scanning, print-camera, and screen-shooting resilient watermarking, has drawn great attention these years. Recent studies show that watermarking with deep networks, e.g., StegaStamp, is particularly suitable for these tasks with strong robustness against the attacks from “physical transmissions” in the wild, although it usually has a relatively small amount of embedding capacity. Recognizing the conventional CNNs are vulnerable to input noise inter-ruptions, the wavelet-based CNNs are adopted in our work by replacing their down-sampling (pooling) and up-sampling layers with Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT), respectively, to learn the stable feature representation from noise-corrupted sam-ples. A new residual regularization loss function incorporating the texture complexity is also proposed to significantly improve the visual quality of watermarked images. Exper-imental results show that the proposed wavelet-based CNN model significantly outperforms the state-of-the-art StegaS-tamp in terms of embedding capacity, imperceptibility, and robustness.

In this letter, we propose a general digital image operation anti-forensic framework based on generative adversarial nets (GANs), called dual-domain generative adversarial network (DDGAN). To tackle the issue of image operation detection, the proposed framework incorporates both operation specific forensic features and machine-learned knowledge to ensure that the generated images exhibit better undetectability performance against various detectors. The DDGAN consists of a generator and two discriminators working on different domains, i.e., the operation-specific feature domain which helps to conceal the artifacts from the perspective of forensic analysis for the target task, and the spatial domain which facilitates to take advantage of machine-learned features from the scratch as a supplementary. Through the experiments on median filtering and JPEG compression anti-forensics, we show the superior performance of the proposed DDGAN compared with state-of-the-art anti-forensic methods in terms of undetectability and visual quality.

With wide applications of facial manipulation technology, fake images and videos are becoming a great public concern. Although existing methods for face forgery detection could achieve fairly good results on public database, most of them perform poorly when the fake images/videos are compressed as they are usually done in social networks. To tackle this issue, a self-supervised decoupling network (SSDN), that incorporates compression irrelevance, is proposed in this paper. The proposed model learns two separate feature representations for the suspect videos, i.e., authenticity and compression. A joint self-supervised strategy is then utilized for feature decoupling, in which, the similarity decoupling is carried out by similarity learning on authentic features, whereas for adversarial decoupling, the proposed SSDN model is trained in an adversarial manner for robust feature learning. Experimental results show that the SSDN outperforms the state-of-the-art methods for deepfake detection against compression attacks on public datasets, e.g., FaceForensics++.

In recent years, many forensic methods are proposed for camera model identification (CMI). These methods expose the acquisition devices of the images according to the traces left during the imaging process. However, such traces could be easily affected by common image operations, e.g., JPEG compression, which make the camera model identification of post-processed images very difficult. In this paper, we propose a GAN based decoupling network (Decoupling-GAN) to boost the performance of CNN-based model detectors for JPEG compressed images by alleviating the effects of JPEG compression on the camera model identification. Through the adversarial training, we rebuilt the consistency of extracted feature maps between the original images and JPEG compressed ones. Experimental results show that Decoupling-GAN exhibits good robustness performance and outperforms prior arts in terms of detection accuracy under JPEG attacks.