Face forgery detection suffers from cross-dataset generalization challenges, where performance degradation occurs due to distribution shifts between training and testing data. Recently, pseudo-fake face generation strategy has mitigated models overfitting to specific forgery traces. However, detectors based on this strategy exhibit an overreliance on blending boundary artifacts for their classification decisions. This overreliance significantly limits their ability to generalize to more advanced face manipulation algorithms, such as FaceDancer and InSwap, which are designed to produce smooth and natural transitions in the blending boundary region. To address this, we propose MAP-Mamba, a novel Multi-Artifacts Perception Mamba framework for modeling generalizable artifact representations from “Generation” to “Enrichment” to “Strengthening”. First, we design an attribute-level face blending method that generate pseudo-fake faces containing fine-grained artifacts via three attribute generators. These pseudo-fakes mimic subtle local inconsistencies in advanced forgery algorithms, guiding the MAP-Mamba to learn diverse forgery features beyond the blending boundary artifacts. Second, considering the variability of face artifacts distribution caused by different forgery algorithms, an artifact style mixing strategy is designed to enrich the artifact style distribution in the training phase by mixing and reorganizing the artifact style features, and to enhance the model’s ability to handle unknown forgery methods. Finally, an adaptive artifact guidance mechanism is proposed to dynamically amplify the artifact-related feature to further strengthen the model’s sensitivity to key artifacts. Extensive experiments on several benchmarks show that MAP-Mamba achieves superior robustness and generalization performance.

With the rapid advancement of face forgery technologies, the quality of manipulated images has significantly improved, posing a severe threat to information security. In response, deepfake detection has emerged as an effective countermeasure against the misuse of these technologies. Sequential deepfake detection,as a specialized extension, targets face images with multi-step manipulation. However, a key challenge in this task is defending against unknown image degradation that occurs during transformation, which is not widely addressed in previous research. This paper introduces a robust detection framework named RSFDF, aimed at enhancing detection capabilities when images are subjected to degradation operations. RSFDF incorporates two critical modules:ATEM and ESCM. ATEM assists the network in focusing on important features while suppressing irrelevant information; ESCM refines the attention mechanism to increase the model’s focus on edge contours, aiding in the judgment of sequential forgeries. Experiments show that RSFDF exhibits significant improvements in robustness against unknown image degradations.

With the rapid development of image generation technologies, especially the advancement of Diffusion Models, the quality of synthesized images has significantly improved, raising concerns among researchers about information security. To mitigate the malicious abuse of diffusion models, diffusion-generated image detection has proven to be an effective countermeasure. However, a key challenge for forgery detection is generalising to diffusion models not seen during training. In this paper, we address this problem by focusing on image noise. We observe that images from different diffusion models share similar noise patterns, distinct from genuine images. Building upon this insight, we introduce a novel Noise-Aware Self-Attention (NASA) module that focuses on noise regions to capture anomalous patterns. To implement a SOTA detection model, we incorporate NASA into Swin Transformer, forming an novel detection architecture NASA-Swin. Additionally, we employ a cross-modality fusion embedding to combine RGB and noise images, along with a channel mask strategy to enhance feature learning from both modalities. Extensive experiments demonstrate the effectiveness of our approach in enhancing detection capabilities for diffusion-generated images. When encountering unseen generation methods, our approach achieves the state-of-the-art performance.

The rapid development of face forgery technology has posed a significant threat to information security. While deepfake detection has proven to be an effective countermeasure, it often struggles to detect fake images generated by unknown forgery methods. Thus, the generalization ability of deepfake detectors to unseen forgery data is a critical concern. Despite many efforts aimed at discovering new forgery artifacts, they often fail to generalize to new manipulation technologies. In this paper, we tackle this challenge by focusing on the difference in texture patterns between training forgeries and unseen forgeries, which can lead to a degradation of generalization. Based on this principle, we propose a new conjecture that encourages deepfake detectors to reduce their sensitivity to forgery texture patterns, thereby improving the detection performance. To this end, we introduce an additional gradient regularization term to the original empirical loss during training. However, computing the Hessian matrix in the gradient calculation process of the regularization term poses a computational complexity. In order to overcome this issue, we optimize the formulation of the gradient regularization term using a first-order approximation method based on Taylor expansion and design a Perturbation Injection Module (PIM) to simplify the implementation process. Additionally, we provide a theoretical analysis from an optimization perspective and explore an interesting aspect of our method. Extensive experiments demonstrate the effectiveness of our approach in improving the generalization ability of deepfake detectors. Importantly, our method is orthogonal to recent advancements in powerful backbones and training data augmentation techniques. When combined with other effective techniques, our method achieves state-of-the-art experimental results.

Maliciously manipulated images and videos, represented by prevalent deepfakes, can easily deceive human and mislead the public opinions. A great deal of effort was spent on detecting these fake images or videos. However, these detection methods always encounter various problems in practical applications. Do we have other ways to block the spread of fake image or videos? This motivates us to focus on an emerging interesting topic, disruption of deepfake generation. We propose the ensemble attacks of various types of deepfake models including facial attribute editing, face swapping and face reenactment models. With the help of hard model mining, we boost the attack success rate significantly comparing with the straightforward average ensemble. Extensive experiments demonstrate the proposed approach can successfully disrupt multiple deepfake models simultaneously under white-box or gray-box attack protocols.

Existing DeepFake detection methods focus on passive detection, i.e., they detect fake face images by exploiting the artifacts produced during DeepFake manipulation. These detection-based methods have their limitation that they only work for ex-post forensics but cannot erase the negative influences of DeepFakes. In this work, we propose a proactive framework for combating DeepFake before the data manipulations. The key idea is to find a well defined substitute latent representation to reconstruct target facial data, leading the reconstructed face to disable the DeepFake generation. To this end, we invert face images into latent codes with a well trained auto-encoder, and search the adversarial face embeddings in their neighbor with the gradient descent method. Extensive experiments on three typical DeepFake manipulation methods, facial attribute editing, face expression manipulation, and face swapping, have demonstrated the effectiveness of our method in different settings.