The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative cross appearance-edge learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an appearance-edge cross-attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at \url{https://github.com/Jenine-321/GenFace
MFVLR: Multi-domain Fine-grained Vision-Language Reconstruction for Generalizable Diffusion Face Forgery Detection and Localization
Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences)（计算机科学与技术学院，齐鲁工业大学（山东省科学院））；School of Computing, National University of Singapore（国立新加坡大学计算机学院）；Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（山东省人工智能研究院，齐鲁工业大学（山东省科学院））；Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology（教育部计算机视觉与系统重点实验室，天津工业大学）
CASIA（中国科学院自动化研究所）；Anhui University（安徽大学）；University of Chinese Academy of Sciences（中国科学院大学）；SenseTime Research（商汤科技研究院）
Recent studies have shown that the performance of forgery detection can be improved with diverse and challenging Deepfakes datasets. However, due to the lack of Deepfakes datasets with large variance in appearance, which can be hardly produced by recent identity swapping methods, the detection algorithm may fail in this situation. In this work, we provide a new identity swapping algorithm with large differences in appearance for face forgery detection. The appearance gaps mainly arise from the large discrepancies in illuminations and skin colors that widely exist in real-world scenarios. Howev…
GAN-Blot: A Controllable Structure-Style Synthesis Benchmark for Western Blot Forensics
Institute of Data Science and Information Computing, National Chung Hsing University（国立中兴大学数据科学与信息计算研究所）；National Taiwan University of Science and Technology（台湾科技大学）
One of the most pressing challenges for the detection of face-manipulated videos is generalising to forgery methods not seen during training while remaining effective under common corruptions such as compression. In this paper, we examine whether we can tackle this issue by harnessing videos of real talking faces, which contain rich information on natural facial appearance and behaviour and are readily available in large quantities online. Our method, termed RealForensics, consists of two stages. First, we exploit the natural correspondence between the visual and auditory modalities in real vi…
Beijing Electronic Science and Technology Institute（北京电子科技学院）；University of Science and Technology Beijing（北京科技大学）
Copy-move forgery detection identifies a tampered image by detecting pasted and source regions in the same image. In this paper, we propose a novel two-stage framework specially for copy-move forgery detection. The first stage is a backbone self deep matching network, and the second stage is named as Proposal SuperGlue. In the first stage, atrous convolution and skip matching are incorporated to enrich spatial information and leverage hierarchical features. Spatial attention is built on self-correlation to reinforce the ability to find appearance similar regions. In the second stage, Proposal …
As the remarkable development of facial manipulation technologies is accompanied by severe security concerns, face forgery detection has become a recent research hotspot. Most existing detection methods train a binary classifier under global supervision to judge real or fake. However, advanced manipulations only perform small-scale tampering, posing challenges to comprehensively capture subtle and local forgery artifacts, especially in high compression settings and cross-dataset scenarios. To address such limitations, we propose a novel framework named Multi-modal Contrastive Classification by…
CAM-VFD: Cross-Attention Multimodal Video Forgery Detection
Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport（计算机工程系，工程与技术学院，阿拉伯科学、技术与海运交通学院）
FairForensics: Seeing Expressions and Parsing Demographics via Vision-Language Modeling for Generalizable Fair Deepfake Detection
Shenzhen University（深圳大学）；Shandong Artificial Intelligence Institute（山东省人工智能研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Tianjin University of Technology（天津理工大学）
AIGC Video Detection based on the fusion of spatial-frequency-optical flow multimodal features
Beihang University（北京航空航天大学）；Nanchang University（南昌大学）；DBAPPSecurity Co., Ltd. (DAS-Security)（杭州安恒信息技术股份有限公司）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
ActivityForensics: A Comprehensive Benchmark for Localizing Manipulated Activity in Videos
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算机与人工智能学院）；Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing（江西省多媒体智能处理重点实验室）；Faculty of Engineering, Shenzhen MSU-BIT University（深圳北理莫斯科大学工程系）；VinUniversity
GazeCLIP: Gaze-Guided CLIP with Adaptive-Enhanced Fine-Grained Language Prompt for Deepfake Attribution and Detection
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shenzhen University（深圳大学）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University（深圳大学广东省智能信息处理重点实验室）；Great Bay University（大湾区大学）
