Peccavi: Visual Paraphrase Attack Safe and Distortion Free Image
  Watermarking Technique for AI-Generated Images
Authors: name: Shreyas Dixit, name: Ashhar Aziz, name: Shashwat Bajpai, name: Vasu Sharma, name: Aman Chadha, name: Vinija Jain, name: Amitava Das

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.