The rapid development of Deepfake technology has enabled the generation of highly realistic manipulated videos, posing severe social and ethical challenges. Existing Deepfake detection methods primarily focused on either spatial or temporal inconsistencies, often neglecting the interplay between the two or suffering from interference caused by natural facial motions. To address these challenges, we propose the global context consistency flow (GC-ConsFlow), a novel dual-stream framework that effectively integrates spatial and temporal features for robust Deepfake detection. The global grouped context aggregation module (GGCA), integrated into the global context-aware frame flow stream (GCAF), enhances spatial feature extraction by aggregating grouped global context information, enabling the detection of subtle, spatial artifacts within frames. The flow-gradient temporal consistency stream (FGTC), rather than directly modeling the residuals, it is used to improve the robustness of temporal feature extraction against the inconsistency introduced by unnatural facial motion using optical flow residuals and gradient-based features. By combining these two streams, GC-ConsFlow demonstrates the effectiveness and robustness in capturing complementary spatiotemporal forgery traces. Extensive experiments show that GC-ConsFlow outperforms existing state-of-the-art methods in detecting Deepfake videos under various compression scenarios.
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）
A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东科学院））；Shandong Artificial Intelligence Institute, China（山东省人工智能研究院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Key Laboratory of Computing Power Network（计算能力网络关键实验室）
Environment-Invariant Subspace Learning for Generalizable Deepfake Detection
Tianjin University of Technology（天津理工大学）；Shandong Artificial Intelligence Institute（山东人工智能研究院）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））
ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors
Department of Informatics, Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学信息学系）；Infocomm Technology Cluster, Singapore Institute of Technology（新加坡科技学院信息通信技术集群）；Department of Applied Physics and Applied Mathematics, Columbia University（哥伦比亚大学应用物理与应用数学系）；Division of Natural and Applied Sciences, Duke Kunshan University（昆山杜克大学自然科学与应用科学部）；Department of Electrical and Computer Engineering, University of Toronto（多伦多大学电气与计算机工程系）
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）
A Multi-Domain Feature Fusion Framework for Generalizable Deepfake Detection Across Different Generators
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan（巴基斯坦国立科技大学电气工程与计算机科学学院）；Department of Communication, Quality Management and Information Systems, Mid Sweden University, Ostersund Campus, Sweden（瑞典中部大学通信、质量管理和信息系统系，厄斯特松德校区）；Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Luleå, Sweden（瑞典吕勒奥理工大学计算机科学、电气与空间工程系）
GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；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）
