Peccavi: Visual Paraphrase Attack Safe and Distortion Free Image
  Watermarking Technique for AI-Generated Images
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.
6864a811d59a9eda59024afe
Shreyas Dixit
6864a811d59a9eda59024aff
Ashhar Aziz
6864a811d59a9eda59024b00
Shashwat Bajpai
6864a811d59a9eda59024b01
Vasu Sharma
6864a811d59a9eda59024b02
Aman Chadha
claimed_verified
2025-07-02T07:38:48.204Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
6864a811d59a9eda59024b03
Vinija Jain
6864a811d59a9eda59024b04
Amitava Das
Peccavi: Visual Paraphrase Attack Safe and Distortion Free Image
  Watermarking Technique for AI-Generated Images
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.
PECCAVI is a robust image watermarking technique designed to withstand visual paraphrase attacks by embedding watermarks in Non-Melting Points using multi-channel frequency domain watermarking and noisy burnishing.
visual paraphrase attack Non-Melting Points multi-channel frequency domain watermarking noisy burnishing model-agnostic