Forgery-aware Adaptive Vision Transformer for Face Forgery Detection

With the advancement in face manipulation technologies, the importance of face forgery detection in protecting authentication integrity becomes increasingly evident. Previous Vision Transformer (ViT)-based detectors have demonstrated subpar performance in cross-database evaluations, primarily because fully fine-tuning with limited Deepfake data often leads to forgetting pre-trained knowledge and over-fitting to data-specific ones. To circumvent these issues, we propose a novel Forgery-aware Adaptive Vision Transformer (FA-ViT). In FA-ViT, the vanilla ViT's parameters are frozen to preserve its pre-trained knowledge, while two specially designed components, the Local-aware Forgery Injector (LFI) and the Global-aware Forgery Adaptor (GFA), are employed to adapt forgery-related knowledge. our proposed FA-ViT effectively combines these two different types of knowledge to form the general forgery features for detecting Deepfakes. Specifically, LFI captures local discriminative information and incorporates these information into ViT via Neighborhood-Preserving Cross Attention (NPCA). Simultaneously, GFA learns adaptive knowledge in the self-attention layer, bridging the gap between the two different domain. Furthermore, we design a novel Single Domain Pairwise Learning (SDPL) to facilitate fine-grained information learning in FA-ViT. The extensive experiments demonstrate that our FA-ViT achieves state-of-the-art performance in cross-dataset evaluation and cross-manipulation scenarios, and improves the robustness against unseen perturbations.

Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection

Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts. However, these methods tend to get trapped in local optima, resulting in limited robustness and generalization capability. To address these issues, we propose a novel Critical Forgery Mining (CFM) framework, which can be flexibly assembled with various backbones to boost their generalization and robustness performance. Specifically, we first build a fine-grained triplet and suppress specific forgery traces through prior knowledge-agnostic data augmentation. Subsequently, we propose a fine-grained relation learning prototype to mine critical information in forgeries through instance and local similarity-aware losses. Moreover, we design a novel progressive learning controller to guide the model to focus on principal feature components, enabling it to learn critical forgery features in a coarse-to-fine manner. The proposed method achieves state-of-the-art forgery detection performance under various challenging evaluation settings.

IStego100K: Large-scale Image Steganalysis Dataset

In order to promote the rapid development of image steganalysis technology, in this paper, we construct and release a multivariable large-scale image steganalysis dataset called IStego100K. It contains 208,104 images with the same size of 1024*1024. Among them, 200,000 images (100,000 cover-stego image pairs) are divided as the training set and the remaining 8,104 as testing set. In addition, we hope that IStego100K can help researchers further explore the development of universal image steganalysis algorithms, so we try to reduce limits on the images in IStego100K. For each image in IStego100K, the quality factors is randomly set in the range of 75-95, the steganographic algorithm is randomly selected from three well-known steganographic algorithms, which are J-uniward, nsF5 and UERD, and the embedding rate is also randomly set to be a value of 0.1-0.4. In addition, considering the possible mismatch between training samples and test samples in real environment, we add a test set (DS-Test) whose source of samples are different from the training set. We hope that this test set can help to evaluate the robustness of steganalysis algorithms. We tested the performance of some latest steganalysis algorithms on IStego100K, with specific results and analysis details in the experimental part. We hope that the IStego100K dataset will further promote the development of universal image steganalysis technology. The description of IStego100K and instructions for use can be found at https://github.com/YangzlTHU/IStego100K