EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection

• URL: http://arxiv.org/abs/2312.08883v1
• Date: Tue, 12 Dec 2023 15:41:24 GMT
• Title: EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection
• Authors: Xuanyu Zhang, Runyi Li, Jiwen Yu, Youmin Xu, Weiqi Li, Jian Zhang
• Abstract summary: We propose a proactive forensics framework EditGuard to unify copyright protection and tamper-agnostic localization. It can offer a meticulous embedding of imperceptible watermarks and precise decoding of tampered areas and copyright information. Our experiments demonstrate that EditGuard balances the tamper localization accuracy, copyright recovery precision, and generalizability to various AIGC-based tampering methods.
• Score: 19.140822655858873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era where AI-generated content (AIGC) models can produce stunning and lifelike images, the lingering shadow of unauthorized reproductions and malicious tampering poses imminent threats to copyright integrity and information security. Current image watermarking methods, while widely accepted for safeguarding visual content, can only protect copyright and ensure traceability. They fall short in localizing increasingly realistic image tampering, potentially leading to trust crises, privacy violations, and legal disputes. To solve this challenge, we propose an innovative proactive forensics framework EditGuard, to unify copyright protection and tamper-agnostic localization, especially for AIGC-based editing methods. It can offer a meticulous embedding of imperceptible watermarks and precise decoding of tampered areas and copyright information. Leveraging our observed fragility and locality of image-into-image steganography, the realization of EditGuard can be converted into a united image-bit steganography issue, thus completely decoupling the training process from the tampering types. Extensive experiments demonstrate that our EditGuard balances the tamper localization accuracy, copyright recovery precision, and generalizability to various AIGC-based tampering methods, especially for image forgery that is difficult for the naked eye to detect. The project page is available at https://xuanyuzhang21.github.io/project/editguard/.

Related papers

• ESpeW: Robust Copyright Protection for LLM-based EaaS via Embedding-Specific Watermark [50.08021440235581]
  Embeds as a Service (Eding) is emerging as a crucial role in AI applications. Eding is vulnerable to model extraction attacks, highlighting the urgent need for copyright protection. We propose a novel embedding-specific watermarking (ESpeW) mechanism to offer robust copyright protection for Eding.
  arXiv  Detail & Related papers  (2024-10-23T04:34:49Z)
• Certifiably Robust Image Watermark [57.546016845801134]
  Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns. We propose the first image watermarks with certified robustness guarantees against removal and forgery attacks.
  arXiv  Detail & Related papers  (2024-07-04T17:56:04Z)
• EnTruth: Enhancing the Traceability of Unauthorized Dataset Usage in Text-to-image Diffusion Models with Minimal and Robust Alterations [73.94175015918059]
  We introduce a novel approach, EnTruth, which Enhances Traceability of unauthorized dataset usage. By strategically incorporating the template memorization, EnTruth can trigger the specific behavior in unauthorized models as the evidence of infringement. Our method is the first to investigate the positive application of memorization and use it for copyright protection, which turns a curse into a blessing.
  arXiv  Detail & Related papers  (2024-06-20T02:02:44Z)
• Protect-Your-IP: Scalable Source-Tracing and Attribution against Personalized Generation [19.250673262185767]
  We propose a unified approach for image copyright source-tracing and attribution. We introduce an innovative watermarking-attribution method that blends proactive and passive strategies. We have conducted experiments using various celebrity portrait series sourced online.
  arXiv  Detail & Related papers  (2024-05-26T15:14:54Z)
• Robust-Wide: Robust Watermarking against Instruction-driven Image Editing [21.69779516621288]
  Malicious users can easily exploit instruction-driven image editing to create fake images. We propose Robust-Wide, the first robust watermarking methodology against instruction-driven image editing. Experiments demonstrate that Robust-Wide can effectively extract the watermark from the edited image with a low bit error rate of nearly 2.6%.
  arXiv  Detail & Related papers  (2024-02-20T03:33:54Z)
• IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI [52.90082445349903]
  Diffusion-based image generation models can create artistic images that mimic the style of an artist or maliciously edit the original images for fake content. Several attempts have been made to protect the original images from such unauthorized data usage by adding imperceptible perturbations. In this work, we introduce a purification perturbation platform, named IMPRESS, to evaluate the effectiveness of imperceptible perturbations as a protective measure.
  arXiv  Detail & Related papers  (2023-10-30T03:33:41Z)
• FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models [64.89896692649589]
  We propose FT-Shield, a watermarking solution tailored for the fine-tuning of text-to-image diffusion models. FT-Shield addresses copyright protection challenges by designing new watermark generation and detection strategies.
  arXiv  Detail & Related papers  (2023-10-03T19:50:08Z)
• JPEG Compressed Images Can Bypass Protections Against AI Editing [48.340067730457584]
  Imperceptible perturbations have been proposed as a means of protecting images from malicious editing. We find that the aforementioned perturbations are not robust to JPEG compression.
  arXiv  Detail & Related papers  (2023-04-05T05:30:09Z)

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.