The proliferation of AIGC-driven face manipulation and deepfakes poses severe threats to media provenance, integrity, and copyright protection. Existing versatile watermarking systems typically rely on embedding explicit localization payloads, which introduces a fidelity--functionality trade-off: larger localization signals degrade visual quality and often reduce decoding robustness under strong generative edits. Moreover, these methods rarely support content recovery, limiting their forensic value when original evidence must be reconstructed. To address these challenges, we present VeriFi, a versatile watermarking framework that unifies copyright protection, pixel-level manipulation localization, and high-fidelity face content recovery. VeriFi makes three key contributions: (1) it embeds a compact semantic latent watermark that serves as a content-preserving prior, enabling faithful restoration even after severe manipulations; (2) it achieves fine-grained localization without dedicated payloads by correlating image features with decoded provenance signals; and (3) it introduces an AIGC attack simulator that combines latent-space mixing with seamless blending to enhance robustness against realistic deepfake pipelines. Extensive experiments on CelebA-HQ and FFHQ demonstrate that VeriFi consistently outperforms state-of-the-art baselines in watermark robustness, localization accuracy, and recovery quality, providing a practical and verifiable defense for deepfake forensics.
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）
TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity
Anhui Province Key Laboratory of Digital Security, University of Science and Technology of China（安徽省数字安全重点实验室，中国科学技术大学）；School of Computing, National University of Singapore（computing学院，新加坡国立大学）
TRLG: Fragile blind quad watermarking for image tamper detection and recovery by providing compact digests with quality optimized using LWT and GA
Ferdowsi University of Mashhad（马什哈德费尔多西大学）；Computer Engineering Department（计算机工程系）；Computer Engineering Department, Ferdowsi University of Mashhad（马什哈德费尔多西大学计算机工程系）
OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peking University Shenzhen Graduate School-Rabbitpre AIGC Joint Research Laboratory（北京大学深圳研究生院Rabbitpre AIGC联合研究实验室）；School of Arts, Peking University（北京大学艺术学院）
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）
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）
MVTamperBench: Evaluating Robustness of Vision-Language Models
Liverpool John Moores University（利物浦约翰·穆里斯大学）；Birla Institute of Technology（比拉理工学院）；Christ University（基督大学）；Columbia University（哥伦比亚大学）；New York University（纽约大学）；University of Washington（华盛顿大学）；Hanyang University（翰阳大学）
Video Signature: Implicit Watermarking for Video Diffusion Models
AI Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能 thrust）；Hong Kong University of Science and Technology（香港科学与技术大学）；School of Software, BNRist, Tsinghua University（清华大学软件学院）
Bit-Level Triangular Content-Aware Permutation for Fragile Image Watermarking: Zero False Positive Rate, Single-Bit Sensitivity, and Arbitrary Dimension Support
Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工大学））；Iran University of Science and Technology（伊朗科技大学）
Removing Watermarks with Partial Regeneration using Semantic Information
International Computer Science Institute（国际计算机科学研究所）；School of Computing and Augmented Intelligence（计算与增强智能学院）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Department of Statistics（统计学系）；University of California at Berkeley（加州大学伯克利分校）
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）
