Peccavi: Visual Paraphrase Attack Safe and Distortion Free Image Watermarking Technique for AI-Generated Images

• URL: http://arxiv.org/abs/2506.22960v1
• Date: Sat, 28 Jun 2025 17:34:08 GMT
• Title: Peccavi: Visual Paraphrase Attack Safe and Distortion Free Image Watermarking Technique for AI-Generated Images
• Authors: Shreyas Dixit, Ashhar Aziz, Shashwat Bajpai, Vasu Sharma, Aman Chadha, Vinija Jain, Amitava Das,
• Abstract summary: This paper introduces PECCAVI, the first visual paraphrase attack-safe and distortion-free image watermarking technique.<n>In visual paraphrase attacks, an image is altered while preserving its core semantic regions, termed Non-Melting Points (NMPs)<n>PECCAVI strategically embeds watermarks within these NMPs and employs multi-channel frequency domain watermarking.
• Score: 6.384378994229647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A report by the European Union Law Enforcement Agency predicts that by 2026, up to 90 percent of online content could be synthetically generated, raising concerns among policymakers, who cautioned that "Generative AI could act as a force multiplier for political disinformation. The combined effect of generative text, images, videos, and audio may surpass the influence of any single modality." In response, California's Bill AB 3211 mandates the watermarking of AI-generated images, videos, and audio. However, concerns remain regarding the vulnerability of invisible watermarking techniques to tampering and the potential for malicious actors to bypass them entirely. Generative AI-powered de-watermarking attacks, especially the newly introduced visual paraphrase attack, have shown an ability to fully remove watermarks, resulting in a paraphrase of the original image. This paper introduces PECCAVI, the first visual paraphrase attack-safe and distortion-free image watermarking technique. In visual paraphrase attacks, an image is altered while preserving its core semantic regions, termed Non-Melting Points (NMPs). PECCAVI strategically embeds watermarks within these NMPs and employs multi-channel frequency domain watermarking. It also incorporates noisy burnishing to counter reverse-engineering efforts aimed at locating NMPs to disrupt the embedded watermark, thereby enhancing durability. PECCAVI is model-agnostic. All relevant resources and codes will be open-sourced.

Related papers

• IConMark: Robust Interpretable Concept-Based Watermark For AI Images [50.045011844765185]
  We propose IConMark, a novel in-generation robust semantic watermarking method.<n>IConMark embeds interpretable concepts into AI-generated images, making it resilient to adversarial manipulation.<n>We demonstrate its superiority in terms of detection accuracy and maintaining image quality.
  arXiv  Detail & Related papers  (2025-07-17T05:38:30Z)
• WMCopier: Forging Invisible Image Watermarks on Arbitrary Images [21.17890218813236]
  We propose WMCopier, an effective watermark forgery attack that operates without requiring prior knowledge of or access to the target watermarking algorithm.<n>Our approach first models the target watermark distribution using an unconditional diffusion model, and then seamlessly embeds the target watermark into a non-watermarked image.<n> Experimental results demonstrate that WMCopier effectively deceives both open-source and closed-source watermark systems.
  arXiv  Detail & Related papers  (2025-03-28T11:11:19Z)
• SEAL: Semantic Aware Image Watermarking [26.606008778795193]
  We propose a novel watermarking method that embeds semantic information about the generated image directly into the watermark.<n>The key pattern can be inferred from the semantic embedding of the image using locality-sensitive hashing.<n>Our results suggest that content-aware watermarks can mitigate risks arising from image-generative models.
  arXiv  Detail & Related papers  (2025-03-15T15:29:05Z)
• Robust and Minimally Invasive Watermarking for EaaS [50.08021440235581]
  Embeds as a Service (Eding) is emerging as a crucial role in AI applications.<n>Eding is vulnerable to model extraction attacks, highlighting the need for copyright protection.<n>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)
• The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks [5.708967043277477]
  We propose a visual paraphraser that can remove watermarks from images. The proposed visual paraphraser operates in two steps. First, it generates a caption for the given image using KOSMOS-2. During the denoising step of the diffusion pipeline, the system generates a visually similar image that is guided by the text caption. Our empirical findings demonstrate that visual paraphrase attacks can effectively remove watermarks from images.
  arXiv  Detail & Related papers  (2024-08-19T22:58:30Z)
• 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)
• WavMark: Watermarking for Audio Generation [70.65175179548208]
  This paper introduces an innovative audio watermarking framework that encodes up to 32 bits of watermark within a mere 1-second audio snippet. The watermark is imperceptible to human senses and exhibits strong resilience against various attacks. It can serve as an effective identifier for synthesized voices and holds potential for broader applications in audio copyright protection.
  arXiv  Detail & Related papers  (2023-08-24T13:17:35Z)
• Invisible Image Watermarks Are Provably Removable Using Generative AI [47.25747266531665]
  Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. We propose a family of regeneration attacks to remove these invisible watermarks. The proposed attack method first adds random noise to an image to destroy the watermark and then reconstructs the image.
  arXiv  Detail & Related papers  (2023-06-02T23:29:28Z)
• Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models [72.9364216776529]
  We propose a novel watermark removal attack from a different perspective. We design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations. Our attack can bypass state-of-the-art watermarking solutions with very high success rates.
  arXiv  Detail & Related papers  (2020-09-18T09:14:54Z)

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.