High-Fidelity Face Content Recovery via Tamper-Resilient Versatile Watermarking

• URL: http://arxiv.org/abs/2603.23940v1
• Date: Wed, 25 Mar 2026 05:02:48 GMT
• Title: High-Fidelity Face Content Recovery via Tamper-Resilient Versatile Watermarking
• Abstract summary: We present VeriFi, a versatile watermarking framework that unifies copyright protection, pixel-level manipulation localization, and high-fidelity face content recovery.<n>It makes three key contributions: (1) it embeds a compact semantic latent watermark that serves as an content-preserving prior, enabling faithful restoration even after severe manipulations; (2) it achieves fine-grained localization without embedding localization-specific artifacts 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 improve robustness to realistic deepfake pipelines.
• Score: 27.912549423920243
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of AIGC-driven face manipulation and deepfakes poses severe threats to media provenance, integrity, and copyright protection. Prior 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, existing 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 an content-preserving prior, enabling faithful restoration even after severe manipulations; (2) it achieves fine-grained localization without embedding localization-specific artifacts 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 improve robustness to realistic deepfake pipelines. Extensive experiments on CelebA-HQ and FFHQ show that VeriFi consistently outperforms strong baselines in watermark robustness, localization accuracy, and recovery quality, providing a practical and verifiable defense for deepfake forensics.

Related papers

• SAiW: Source-Attributable Invisible Watermarking for Proactive Deepfake Defense [7.442956390854273]
  Deepfakes generated by modern generative models pose a serious threat to information integrity, digital identity, and public trust.<n>We introduce SAiW, a Source-Attributed Invisible watermarking Framework for proactive deepfake defense and media provenance verification.
  arXiv  Detail & Related papers  (2026-03-24T13:26:52Z)
• RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces [16.612226216769262]
  We propose RecoverMark, a watermarking framework that achieves robust manipulation localization, content recovery, and ownership verification simultaneously.<n>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.<n>Based on these insights, RecoverMark treats the protected face content itself as the watermark and embeds it into the surrounding background.
  arXiv  Detail & Related papers  (2026-02-24T07:11:40Z)
• AuthenLoRA: Entangling Stylization with Imperceptible Watermarks for Copyright-Secure LoRA Adapters [52.556959321030966]
  Low-Rank Adaptation (LoRA) offers an efficient paradigm for customizing diffusion models.<n>Existing watermarking techniques either target base models or verify LoRA modules themselves.<n>We propose AuthenLoRA, a unified watermarking framework that embeds imperceptible, traceable watermarks directly into the LoRA training process.
  arXiv  Detail & Related papers  (2025-11-26T09:48:11Z)
• StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models [55.05404953041403]
  We propose a novel framework that seamlessly integrates a binary watermark into the diffusion generation process.<n>We show that StableGuard consistently outperforms state-of-the-art methods in image fidelity, watermark verification, and tampering localization.
  arXiv  Detail & Related papers  (2025-09-22T16:35:19Z)
• TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity [76.98973481600002]
  This paper proposes a Tamper-Aware Generative image WaterMarking method named TAG-WM.<n>The proposed method comprises four key modules: a dual-mark joint sampling (DMJS) algorithm for embedding copyright and localization watermarks into the latent space while preserving generative quality.<n>The experimental results demonstrate that TAG-WM achieves state-of-the-art performance in both tampering robustness and localization capability even under distortion.
  arXiv  Detail & Related papers  (2025-06-30T03:14:07Z)
• A Watermark for Auto-Regressive Image Generation Models [50.599325258178254]
  We propose C-reweight, a distortion-free watermarking method explicitly designed for image generation models.<n>C-reweight mitigates retokenization mismatch while preserving image fidelity.
  arXiv  Detail & Related papers  (2025-06-13T00:15:54Z)
• GenPTW: In-Generation Image Watermarking for Provenance Tracing and Tamper Localization [32.843425702098116]
  GenPTW is an In-Generation image watermarking framework for latent diffusion models (LDMs)<n>It embeds structured watermark signals during the image generation phase, enabling unified provenance tracing and tamper localization.<n>Experiments demonstrate that GenPTW outperforms existing methods in image fidelity, watermark extraction accuracy, and tamper localization performance.
  arXiv  Detail & Related papers  (2025-04-28T08:21:39Z)
• Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models [66.54457339638004]
  Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models.<n>We propose a diffusion model watermarking method tailored for real-world deployment.<n>Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness.
  arXiv  Detail & Related papers  (2025-04-21T11:18:16Z)
• Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal [57.84348166457113]
  We introduce a novel feature adapting framework that leverages the representation capacity of a pre-trained image inpainting model.<n>Our approach bridges the knowledge gap between image inpainting and watermark removal by fusing information of the residual background content beneath watermarks into the inpainting backbone model.<n>For relieving the dependence on high-quality watermark masks, we introduce a new training paradigm by utilizing coarse watermark masks to guide the inference process.
  arXiv  Detail & Related papers  (2025-04-07T02:37:14Z)
• Robust Identity Perceptual Watermark Against Deepfake Face Swapping [9.402982368385569]
  Deepfake face swapping has caused critical privacy issues with the rapid development of deep generative models.<n>We propose a robust identity perceptual watermarking framework that concurrently performs detection and source tracing against Deepfake face swapping.
  arXiv  Detail & Related papers  (2023-11-02T16:04:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.