Most of previous deepfake detection researches bent their efforts to describe and discriminate artifacts in human perceptible ways, which leave a bias in the learned networks of ignoring some critical invariance features intra-class and underperforming the robustness of internet interference. Essentially, the target of deepfake detection problem is to represent natural faces and fake faces at the representation space discriminatively, and it reminds us whether we could optimize the feature extraction procedure at the representation space through constraining intra-class consistence and inter-class inconsistence to bring the intra-class representations close and push the inter-class representations apart? Therefore, inspired by contrastive representation learning, we tackle the deepfake detection problem through learning the invariant representations of both classes and propose a novel real-centric consistency learning method. We constraint the representation from both the sample level and the feature level. At the sample level, we take the procedure of deepfake synthesis into consideration and propose a novel forgery semantical-based pairing strategy to mine latent generation-related features. At the feature level, based on the centers of natural faces at the representation space, we design a hard positive mining and synthesizing method to simulate the potential marginal features. Besides, a hard negative fusion method is designed to improve the discrimination of negative marginal features with the help of supervised contrastive margin loss we developed. The effectiveness and robustness of the proposed method has been demonstrated through extensive experiments.
Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）
Evading DeepFake Detectors via Adversarial Statistical Consistency
Kyushu University（九州大学）；Centre for Frontier AI Research (CFAR), A*STAR（新加坡科技研究局前沿人工智能研究中心）；Institute of High Performance Computing (IHPC), A*STAR（新加坡科技研究局高性能计算研究所）；Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；University of Alberta（阿尔伯塔大学）；The University of Tokyo（东京大学）
WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks
College of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center（新疆多模态智能处理与信息安全工程技术研究中心）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Computer Science, Hefei University of Technology（合肥工业大学计算机科学与技术学院）
LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection
The Hong Kong Polytechnic University（香港理工大学）；University College London（伦敦大学学院）；Tsinghua University（清华大学）；Sun Yat-sen University（中山大学）；National University of Singapore（新加坡国立大学）
LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection
CVI SnT University of Luxembourg（卢森堡大学CVI SnT研究所）；Cristal Laboratory National School of Computer Sciences University of Manouba（马努巴大学国家计算机科学学院Cristal实验室）
ED$^4$: Explicit Data-level Debiasing for Deepfake Detection
School of computer science, Wuhan University（武汉大学计算机学院）；WeChat, Tencent Inc.（腾讯公司）；School of electronic and computer engineering, Peking University Shenzhen Graduate School（北京大学深圳研究生院电子与计算机工程学院）
Fact or Fake? Assessing the Role of Deepfake Detectors in Multimodal Misinformation Detection
The University of Western Australia（西澳大学）；Fatih Sultan Mehmet Vakif University（法提赫·苏丹·梅赫梅特·瓦基夫大学）；Aljazeera Media Network Investigative Department（半岛电视台调查部门）
All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark
School of Computer Science and Technology, Xinjiang University, Urumqi, China（新疆大学计算机科学与技术学院）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center, Urumqi, China（新疆多模态智能处理与信息安全工程技术研究中心）
DeepfakeBench-MM: A Comprehensive Benchmark for Multimodal Deepfake Detection
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Towards Robust DeepFake Detection under Unstable Face Sequences: Adaptive Sparse Graph Embedding with Order-Free Representation and Explicit Laplacian Spectral Prior
Institute of Intelligent Systems, College of Artificial Intelligence and Green Energy, National Yang Ming Chiao Tung University（智能系统研究所，人工智能与绿色能源学院，国立阳明交通大学）；Institute of Data Science, National Cheng Kung University（数据科学研究所，国立成功大学）
Generalizable Deepfake Detection Based on Forgery-aware Layer Masking and Multi-artifact Subspace Decomposition
Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology（南京信息工程大学数字取证教育部工程研究中心）
SPARK-IL: Spectral Retrieval-Augmented RAG for Knowledge-driven Deepfake Detection via Incremental Learning
Univ. Polytechnique Hauts-de-France（上法兰西理工大学）；Institute of Applied Sciences and Intelligent Systems – Lecce（应用科学与智能系统研究所（莱切））；Department of Innovation Engineering, University of Salento（萨伦托大学创新工程系）；Sorbonne Center for Artificial Intelligence, Sorbonne University Abu Dhabi（索邦大学阿布扎比分校索邦人工智能中心）
Inconsistency-aware Multimodal Schrödinger Bridge for Deepfake Localization
Department of Computer Science and Techonology, Huaqiao University（华侨大学计算机科学与技术系）；Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University（厦门计算机视觉与模式识别重点实验室）；Tongji University（同济大学）；School of Cyber Science and Engineering, Wuhan University（武汉大学网络空间安全学院）
Cycle Consistency in Video Object-Centric Learning
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland（艾洛大学电气工程与自动化系，芬兰 Espoo）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China（华中科技大学人工智能与自动化学院，中国 Wuhan）；Department of Computer Science, Aalto University, Espoo, Finland（艾洛大学计算机科学系，芬兰 Espoo）；Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland（奥卢大学机器视觉与信号分析中心，芬兰 Oulu）
Robust Identity Perceptual Watermark Against Deepfake Face Swapping
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)（山东人工智能研究院，齐鲁工业大学（山东科学院））；School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东科学院）网络安全学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, China（教育部计算能力网络与信息安全重点实验室）
Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases
Delft University of Technology（代尔夫特理工大学）；The University of Tokyo（东京大学）；Mila, University of Montreal（蒙特利尔大学米拉研究所）；Google DeepMind（谷歌DeepMind）；Agency for Science, Technology and Research（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）
