11 matches found
reverse-SynthID-text
SynthID Watermark Reverse Engineering Overview This directory contains tools for analyzing and removing SynthID watermarks from AI-generated text. The watermark was developed by Google DeepMind and published in Nature 2024. How SynthID Works Watermarking Process 1. N-gram Context : For each token...
remove-ai-watermarks
Remove AI Watermarks Remove AI provenance marks from images and video you generated yourself: known visible labels such as the Google Gemini sparkle watermark and vendor text marks; invisible pixel watermarks through diffusion regeneration; C2PA, EXIF, XMP, IPTC, and related AI metadata. Video...
reverse-SynthID
Reverse-Engineering SynthID Discovering, detecting, and surgically removing Google's AI watermark through spectral analysis Visit us on PitchHut This fork adds a drag and drop desktop app for the V3 bypass, no command line needed after setup. See gui/README.md for setup and usage. What the...
remove-ai-watermarks v0.37.1
Remove AI Watermarks Remove AI provenance marks from images and video you generated yourself: known visible labels such as the Google Gemini sparkle watermark and vendor text marks; invisible pixel watermarks through diffusion regeneration; C2PA, EXIF, XMP, IPTC, and related AI metadata. Video...
HarmonicAttack: An Adaptive Cross-Domain Audio Watermark Removal
The availability of high-quality, AI-generated audio raises security challenges such as misinformation campaigns and voice-cloning fraud. A key defense against the misuse of AI-generated audio is by watermarking it, so that it can be easily distinguished from genuine audio. As those seeking to...
RLCracker: Exposing the Vulnerability of LLM Watermarks with Adaptive RL Attacks
Large Language Models LLMs watermarking has shown promise in detecting AI-generated content and mitigating misuse, with prior work claiming robustness against paraphrasing and text editing. In this paper, we argue that existing evaluations are not sufficiently adversarial, obscuring critical...
Removing Box-Free Watermarks for Image-To-Image Models Via Query-Based Reverse Engineering
The intellectual property of deep generative networks GNets can be protected using a cascaded hiding network HNet which embeds watermarks or marks into GNet outputs, known as box-free watermarking. Although both GNet and HNet are encapsulated in a black box called operation network, or ONet, with...
VideoMarkBench: Benchmarking Robustness of Video Watermarking
The rapid development of video generative models has led to a surge in highly realistic synthetic videos, raising ethical concerns related to disinformation and copyright infringement. Recently, video watermarking has been proposed as a mitigation strategy by embedding invisible marks into...
Towards Dataset Copyright Evasion Attack against Personalized Text-To-Image Diffusion Models
Text-to-image T2I diffusion models have rapidly advanced, enabling high-quality image generation conditioned on textual prompts. However, the growing trend of fine-tuning pre-trained models for personalization raises serious concerns about unauthorized dataset usage. To combat this, dataset...
Watermark Overwriting Attack on StegaStamp Algorithm
This paper presents an attack method on the StegaStamp watermarking algorithm that completely removes watermarks from an image with minimal quality loss, developed as part of the NeurIPS "Erasing the invisible" competition...
