RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces
Haonan An, Xiaohui Ye, Guang Hua, Yihang Tao, Hangcheng Cao, Xiangyu Yu, Yuguang Fang
摘要
The proliferation of AI-generated content (AIGC) has facilitated sophisticated face manipulation, severely undermining visual integrity and posing unprecedented challenges to intellectual property (IP). In response, a common proactive defense leverages fragile watermarks to detect, localize, or even recover manipulated regions. However, these methods always assume an adversary unaware of the embedded watermark, overlooking their inherent vulnerability to watermark removal attacks. Furthermore, this fragility is exacerbated in the commonly used dual-watermark strategy that adds a robust watermark for image ownership verification, where mutual interference and limited embedding capacity reduce the fragile watermark's effectiveness.To address the gap, we propose RecoverMark, a watermarking framework that achieves robust manipulation localization, content recovery, and ownership verification simultaneously. Our key insight is twofold. First, we exploit a critical real-world constraint: an adversary must preserve the background's semantic consistency to avoid visual detection, even if they apply global, imperceptible watermark removal attacks. Second, using the image's own content (face, in this paper) as the watermark enhances extraction robustness. Based on these insights, RecoverMark treats the protected face content itself as the watermark and embeds it into the surrounding background. By designing a robust two-stage training paradigm with carefully crafted distortion layers that simulate comprehensive potential attacks and a progressive training strategy, RecoverMark achieves a robust watermark embedding in no fragile manner for image manipulation localization, recovery, and image IP protection simultaneously. Extensive experiments demonstrate the proposed RecoverMark's robustness against both seen and unseen attacks and its generalizability to in-distribution (ID) and out-of-distribution (OOD) data. Code will be released upon acceptance.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper14
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Invisible Image Watermarks Are Provably Removable Using Generative AIXuandong Zhao, Kexun Zhang, Zihao Su, Saastha Vasan 等NeurIPS 2024 · 被引用 209 次
- Localization of Deep Inpainting Using High-Pass Fully Convolutional NetworkHaodong Li, Jiwu HuangICCV 2019 · 被引用 157 次
- FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance TrackingRun Wang, Felix Juefei-Xu, Meng Luo, Yang Liu 等ACM MM 2021 · 被引用 77 次
- EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright ProtectionXuanyu Zhang, Runyi Li, Jiwen Yu, Youmin Xu 等CVPR 2024 · 被引用 58 次
相关 Paper
- Attack-Resistant Watermarking for AIGC Image Forensics via Diffusion-based Semantic DeflectionQingyu Liu, Yitao Zhang, Zhongjie Ba, Chao Shuai 等ICLR 2026 · 被引用 2 次
- LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual WatermarksTianyi Wang, Mengxiao Huang, Harry Cheng, Xiao Zhang 等ACM MM 2024 · 被引用 27 次
- TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion SensitivityYuzhuo Chen, Zehua Ma, Han Fang, Weiming Zhang 等ICCV 2025 · 被引用 4 次
- Proactive Deepfake Detection via Self-Verifiable Semantic WatermarkingPeiqi Jiang, Bohan Lei, Yuhao Sun, Lingyun Yu 等ACM MM 2025
- FractalForensics: Proactive Deepfake Detection and Localization via Fractal WatermarksTianyi Wang, Harry Cheng, Ming-Hui Liu, Mohan KankanhalliACM MM 2025 · 被引用 7 次
