RoboSignature: Robust Signature and Watermarking on Network Attacks
Generative models have enabled easy creation and generation of images of all kinds given a single prompt. However, this has also raised ethical concerns about what is an actual piece of content created by humans or cameras compared to model-generated content like images or videos. Watermarking data generated by modern generative models is a popular method to provide information on the source of the content. The goal is for all generated images to conceal an invisible watermark, allowing for future detection or identification. The Stable Signature finetunes the decoder of Latent Diffusion Models such that a unique watermark is rooted in any image produced by the decoder. In this paper, we present a novel adversarial fine-tuning attack that disrupts the model's ability to embed the intended watermark, exposing a significant vulnerability in existing watermarking methods. To address this, we further propose a tamper-resistant fine-tuning algorithm inspired by methods developed for large language models, tailored to the specific requirements of watermarking in LDMs. Our findings emphasize the importance of anticipating and defending against potential vulnerabilities in generative systems.
Code (1)
Tasks
DecoderMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Signature vs. Substance: Evaluating the Balance of Adversarial Resistance and Linguistic Quality in Watermarking Large Language Models
To mitigate the potential harms of Large Language Models (LLMs)generated text, researchers have proposed watermarking, a process of embedding detectable signals within text. With watermarking, we can always accurately de…
SWaRL: Safeguard Code Watermarking via Reinforcement Learning
We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLMs by embedding unique and verifiable signatures in the generated program. Existing waterm…
Reinforcement LearningBileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature
Text watermarks for large language models (LLMs) have been commonly used to identify the origins of machine-generated content, which is promising for assessing liability when combating deepfake or harmful content. While …
Face SwappingREADME: Robust Error-Aware Digital Signature Framework via Deep Watermarking Model
Deep learning-based watermarking has emerged as a promising solution for robust image authentication and protection. However, existing models are limited by low embedding capacity and vulnerability to bit-level errors, m…
DeepEclipse: How to Break White-Box DNN-Watermarking Schemes
Deep Learning (DL) models have become crucial in digital transformation, thus raising concerns about their intellectual property rights. Different watermarking techniques have been developed to protect Deep Neural Networ…