With the rapid development of deep learning technology, more and more face forgeries by deepfake are widely spread on social media, causing serious social concern. Face forgery detection has become a research hotspot in recent years, and many related methods have been proposed until now. For those images with low quality and/or diverse sources, however, the detection performances of existing methods are still far from satisfactory. In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection. Different from the middle flow in original Xception model, we try to catch different high-semantic features of the face images using different levels of convolution, and introduce the convolutional block attention module and feature fusion to refine and reorganize those high-semantic features. In the exit flow, we employ the self-attention mechanism and depthwise separable convolution to learn the global information and local information of the fused features separately to improve the classification the ability of the proposed model. Experimental results evaluated on three Deepfake datasets demonstrate that the proposed method outperforms Xception as well as other related methods both in effectiveness and generalization ability.
The University of Sydney, Sydney, Australia（悉尼大学）；Xiamen University, Xiamen, China（厦门大学）；Hong Kong Polytechnic University, HongKong, China（香港理工大学）；National University of Singapore, Singapore, Singapore（新加坡国立大学）
FOCA: Frequency-Oriented Cross-Domain Forgery Detection, Localization and Explanation via Multi-Modal Large Language Model
Harbin Institute of Technology（哈尔滨工业大学）；DZ-Matrix；Guangdong Laboratory of Artificial Intelligence and Digital Economy（广东省人工智能与数字经济实验室）；Chongqing Research Institute of HIT（哈尔滨工业大学重庆研究院）；University of New South Wales（新南威尔士大学）
SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
School of Geography and Remote Sensing, Guangzhou University（广州大学地理科学与遥感学院）；Xiamen University Of Technology（厦门理工学院）；University of Illinois at Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校）；CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校CyberGIS研究中心）；School of Geosciences and Info-Physics, Central South University（中南大学地球科学与信息物理学院）
DBCF: Dual-Branch Complementary Fusion of Foundation Models for Generalized Deepfake Detection
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Ocean University of China（中国海洋大学）
Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction
Vignan's Foundation for Science Technology and Research（维格南科学技术研究基金会）；University of Northern British Columbia（北不列颠哥伦比亚大学）；Hankuk University of Foreign Studies（韩国外国语大学）；VIT（韦洛尔理工学院）；Chungbuk National University（忠北国立大学）
A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases
Washington University of Science and Technology（华盛顿科技大学）；Kansas State University（堪萨斯州立大学）；State University of New York Polytechnic Institute（纽约州立大学理工学院）；University of Alaska Anchorage（阿拉斯加大学安克雷奇分校）
CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection
School of Software Technology, Zhejiang University（浙江大学软件学院）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Ant Group（蚂蚁集团）；School of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）
DDNet: A Dual-Stream Graph Learning and Disentanglement Framework for Temporal Forgery Localization
College of Cyber Science and Technology, Hunan University（计算机科学与技术学院，湖南大学）；School of Computer Science and Technology, Changsha University of Science & Technology（计算机科学与技术学院，长沙理工大学）
Deepfake Forensics Adapter: A Dual-Stream Network for Generalizable Deepfake Detection
Shenzhen Technology University（深圳技术大学）；Singapore Institute of Technology（新加坡理工学院）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）
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）
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）
Morphology-optimized Multi-Scale Fusion: Combining Local Artifacts and Mesoscopic Semantics for Deepfake Detection and Localization
The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Curvelet-Based Frequency-Aware Feature Enhancement for Deepfake Detection
Department of Computer Science, College of Science, University of Zakho（扎赫大学计算机科学系）；PRIME Lab, Scientific Research Center, University of Zakho（扎赫大学PRIME实验室）
Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS部门）；Sangfor Technologies Inc.（Sangfor技术公司）；China Mobile Financial Technology Co., Ltd.（中国移动金融科技有限公司）；CAIR, HKSIS, Chinese Academy of Sciences（中国科学院CAIR、HKSIS部门）；SCSE, FIE, M.U.S.T, Macau, China（澳门SCSE、FIE、M.U.S.T部门）；Vast Intelligence Lab, Sydney, Australia（悉尼澳大利亚Vast Intelligence Lab）
Ensemble Deep Learning Approaches for AI-Altered Video Detection
Department of Mathematical and Computational Sciences, University of Toronto Mississauga（多伦多大学密西沙加分校数学与计算科学系）；Department of Mathematical and Computational Sciences, University of Toronto（多伦多大学数学与计算科学系）；University of Toronto（多伦多大学）
