The explosive growth of generative AI has saturated the internet with AI-generated images, raising security concerns and increasing the need for reliable detection methods. The primary requirement for such detection is generalizability, typically achieved by training on numerous fake images from various models. However, practical limitations, such as closed-source models and restricted access, often result in limited training samples. Therefore, training a general detector with few-shot samples is essential for modern detection mechanisms. To address this challenge, we propose FAMSeC, a general AI-generated image detection method based on LoRA-basedForgeryAwarenessModule andSemantic feature-guidedContrastive learning strategy. To effectively learn from limited samples and prevent overfitting, we developed a forgery awareness module (FAM) based on LoRA, maintaining the generalization of pre-trained features. Additionally, to cooperate with FAM, we designed a semantic feature-guided contrastive learning strategy (SeC), making the FAM focus more on the differences between real/fake image than on the features of the samples themselves. Experiments show that FAMSeC outperforms state-of-the-art method, enhancing classification accuracy by 14.55% with just 0.56% of the training samples.

With the rapid development in media generation technologies, the creation of DeepFake videos is within everyone’s reach. As the widespread diffusion of DeepFakes can lead to severe consequences (e.g., defamation, fake news spreading, etc.), detecting DeepFakes is becoming a crucial task within the forensic community. However, most of the existing DeepFake detectors suffer from two issues: i) they are hardly explainable as they build upon black-box data-driven techniques rather than interpretable features; ii) they are often tailored to low-level texture features, failing to generalize on low-quality DeepFake videos. In this work we propose a video DeepFake detector that aims at solving these issues. The proposed detector relies on the fact that most DeepFake generators work on a frame-by-frame basis, thus breaking the temporal consistency of facial features across frames. In particular, we noticed that facial identity features tend to be less stable in time on DeepFake videos than original ones. We therefore propose a framework trained on time series of facial identity features. The use of high-level semantic features makes the detector interpretable and robust against low-quality DeepFake videos. Extensive experiments show that our method achieves outstanding performance on low-quality DeepFake video and obtains promising results on unseen dataset evaluation. The code is available at https://github.com/HongguLiu/Identity-Inconsistency-DeepFake-Detection

The rapid progress of the DeepFake technique has caused severe privacy problems. Thus protecting facial data against DeepFake becomes an urgent requirement. Face protection can be regarded as a bidirectional process: Face-out-detection (FOD) and Face-in-forensics (FIF). For FOD, the detectability should be satisfied when using the protected face to replace other faces. For FIF, traceability should be guaranteed when the protected face is replaced by others. For this, we propose a Bidirectional Facial-data Protection Framework (BiFPro) to protect face data comprehensively. This framework is composed of three main parts: Watermarking embedding, Face-out-detection (FOD) and Face-in-forensics (FIF). For the FOD case, we ensure the vulnerability of the original face by embedding fragile watermarking. Once the protected facial image is used to replace other faces, the watermarking information will be corrupted in the synthesized face images which can be used to detect the authenticity of the protected facial images. As for the FIF case, we guarantee the traceability of the protected face image by embedding robust watermarking, with which the fake faces can be traced with the reserved watermarking even after the face is swapped. Experimental results demonstrate that our proposed BiFPro could generate the watermarking which is fragile to FOD and at the same time robust to FIF with an average watermark extraction success rate reaching more than 95% when defending against the four advanced DeepFake techniques. Finally, we hope this work can encourage more initiative countermeasures against DeepFake.

Recently almost all the mainstream deepfake detection methods use Convolutional Neural Networks (CNN) as their backbone. However, due to the overreliance on local texture information which is usually determined by forgery methods of training data, these CNN-based methods cannot generalize well to unseen data. To get out of the predicament of prior methods, in this paper, we propose a novel transformer-based framework to model both global and local information and analyze anomalies of face images. In particular, we design attention leading module, multi-forensics module and variant residual connections for deepfake detection, and leverage token-level contrast loss for more detailed supervision. Experiments on almost all popular public deepfake datasets demonstrate that our method achieves state-of-the-art performance in cross-dataset evaluation and comparable performance in intra-dataset evaluation.

With the rapid development of digital devices, the issue of how to transmit information among different devices with multimedia carriers has drawn much attention from the research community. This paper focuses on the important user scenario of “screen-to-camera information transmission”. Along this direction, image coding-based techniques have been shown to be the most popular and effective methods in the past decades. However, after careful study, we find that none of the existing methods can satisfy the four important properties simultaneously, i.e.,high transparency,high embedding efficiency,strong transmission robustnessandhigh adaptability to device types. This is mainly because these properties are contradictory with each other. In this paper, we thus propose a screen-to-camera image code dubbed “TERA” (transparency,efficiency,robustness andadaptability), which makes it possible to circumvent the contradiction among the above four properties for the first time. Generally, TERA adopts the color decomposition principle to ensure the visual quality and the superposition-based scheme to ensure embedding efficiency. BCH-coding-based information arrangement and a powerful attention-guided information decoding network are further designed to guarantee the robustness and adaptability. Through extensive experiments, the superiority and broad applications of our method are demonstrated.

The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step of most face forgery techniques, and cumulative up-sampling will result in obvious changes in the frequency domain, especially in the phase spectrum. According to the property of natural images, the phase spectrum preserves abundant frequency components that provide extra information and complement the loss of the amplitude spectrum. To this end, we present a novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery to improve the transferability, for face forgery detection. And we also theoretically analyze the validity of utilizing the phase spectrum. Moreover, we notice that local texture information is more crucial than high-level semantic information for the face forgery detection task. So we reduce the receptive fields by shallowing the network to suppress high-level features and focus on the local region. Extensive experiments show that SPSL can achieve the state-of-the-art performance on cross-datasets evaluation as well as multi-class classification and obtain comparable results on single dataset evaluation.