With the significant advances in deep generative models for image and video synthesis, Deepfakes and manipulated media have raised severe societal concerns. Conventional machine learning classifiers for deepfake detection often fail to cope with evolving deepfake generation technology and are susceptible to adversarial attacks. Alternatively, invisible image watermarking is being researched as a proactive defense technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. A handful of invisible image watermarking techniques introduced for media authentication have proven vulnerable to basic image processing operations and watermark removal attacks. In response, we have proposed a semi-fragile image watermarking technique that embeds an invisible secret message into real images for media authentication. Our proposed watermarking framework is designed to be fragile to facial manipulations or tampering while being robust to benign image-processing operations and watermark removal attacks. This is facilitated through a unique architecture of our proposed technique consisting of critic and adversarial networks that enforce high image quality and resiliency to watermark removal efforts, respectively, along with the backbone encoder-decoder and the discriminator networks. Thorough experimental investigations on SOTA facial Deepfake datasets demonstrate that our proposed model can embed a $64$-bit secret as an imperceptible image watermark that can be recovered with a high-bit recovery accuracy when benign image processing operations are applied while being non-recoverable when unseen Deepfake manipulations are applied. In addition, our proposed watermarking technique demonstrates high resilience to several white-box and black-box watermark removal attacks. Thus, obtaining state-of-the-art performance.
Amrita School of Computing Amrita Vishwa Vidyapeetham Amaravati(Andhra Pradesh), India；Engineering Thapar Institute of Engineering \& Technology Patiala(Punjab), India
DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement Learning
The Signal Processing, Artificial Intelligence and Vision Technologies (SAIVT), Queensland University of Technology, Australia（信号处理、人工智能与视觉技术研究所（SAIVT），昆士兰理工大学）
LAVA: Layered Audio-Visual Anti-tampering Watermarking for Robust Deepfake Detection and Localization
School of Computer Science and Engineering（计算机科学与工程学院）；UNSW Sydney（新南威尔士大学悉尼分校）；School of Information and Communication Technology（信息与通信技术学院）；Griffith University（格里菲斯大学）
SpectraNet: FFT-assisted Deep Learning Classifier for Deepfake Face Detection
Department of Electronic and Telecommunications Engineering, University of Moratuwa（电子与电信工程系，穆拉图瓦大学）；School of Electrical and Data Engineering, University of Technology Sydney（电气与数据工程学院，技术悉尼大学）
Fudan University（复旦大学）；Chinese Academy of Sciences（中国科学院）；Duke University（杜克大学）
Existing deepfake-detection methods focus on passive detection, i.e., they detect fake face images via exploiting the artifacts produced during deepfake manipulation. A key limitation of passive detection is that it cannot detect fake faces that are generated by new deepfake generation methods. In this work, we propose FaceGuard, a proactive deepfake-detection framework. FaceGuard embeds a watermark into a real face image before it is published on social media. Given a face image that claims to be an individual (e.g., Nicolas Cage), FaceGuard extracts a watermark from it and predicts the face …
University of California, Berkeley（加州大学伯克利分校）；Columbia University（哥伦比亚大学）；Cornell University（康奈尔大学）；Anthropic；Oregon State University（俄勒冈州立大学）；Google DeepMind（谷歌DeepMind）；ETH Zurich（苏黎世联邦理工学院）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Carnegie Mellon University（卡内基梅隆大学）；University of California, San Diego（加州大学圣地亚哥分校）
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）
Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges
University of Nevada, Reno（内华达大学雷诺分校）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）；Institute of High Performance Computing (IHPC)（高性能计算研究所）；Centre for Advanced Technologies in Online Safety (CATOS)（网络安全先进技术中心）
Peking University（北京大学）；State Key Laboratory of Media Convergence Production Technology and Systems（媒体融合生产技术与系统国家重点实验室）；Fudan University（复旦大学）；Ant Group（蚂蚁集团）；Nanyang Technological University（南洋理工大学）
Malicious applications of deepfakes (i.e., technologies generating target facial attributes or entire faces from facial images) have posed a huge threat to individuals' reputation and security. To mitigate these threats, recent studies have proposed adversarial watermarks to combat deepfake models, leading them to generate distorted outputs. Despite achieving impressive results, these adversarial watermarks have low image-level and model-level transferability, meaning that they can protect only one facial image from one specific deepfake model. To address these issues, we propose a novel solut…
Whether, Which, and Whose: Solving the Triple Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios
College of Computer Science and Technology（计算机科学与技术学院）；Xinjiang University（新疆大学）；College of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；Hunan University（湖南大学）
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）
San Diego State University（圣地亚哥州立大学）；University of California San Diego（加州大学圣地亚哥分校）
Deepfakes and manipulated media are becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. There have been several attempts at combating Deepfakes using machine learning classifiers. However, such classifiers do not generalize well to black-box image synthesis techniques and have been shown to be vulnerable to adversarial examples. To address these challenges, we introduce a deep learning based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. Instead of …
DiffMark: Diffusion-based Robust Watermark Against Deepfakes
College of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Silk Road Multilingual Cognitive Computing International Cooperation Joint Laboratory（丝绸之路多语种认知计算国际合作联合实验室）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；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）；Department of Computer Science, State University of New York（纽约州立大学计算机科学系）
Deepfakes and Synthetic Media: Generation, Detection, and Governance
Heriot-Watt University（赫瑞-瓦特大学）；Democritus University of Thrace（德谟克利特色雷斯大学）；National and Technical University of Athens（雅典国立技术大学）；Salvezza Energy Systems Ltd.（Salvezza能源系统有限公司）；Hellenic Open University（希腊开放大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Technical University of Sofia（索非亚技术大学）；Hellenic Naval Academy（希腊海军学院）
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）
