VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models
- URL: http://arxiv.org/abs/2312.00057v2
- Date: Tue, 2 Apr 2024 14:28:26 GMT
- Title: VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models
- Authors: Xiang Li, Qianli Shen, Kenji Kawaguchi,
- Abstract summary: We introduce Virtually Assured Amplification Attack (VA3), a novel online attack framework.
VA3 amplifies the probability of generating infringing content on the sustained interactions with generative models.
These findings highlight the potential risk of implementing probabilistic copyright protection in practical applications of text-to-image generative models.
- Score: 27.77911368516792
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content. While probabilistic copyright protection methods provide a probabilistic guarantee against such infringement, in this paper, we introduce Virtually Assured Amplification Attack (VA3), a novel online attack framework that exposes the vulnerabilities of these protection mechanisms. The proposed framework significantly amplifies the probability of generating infringing content on the sustained interactions with generative models and a non-trivial lower-bound on the success probability of each engagement. Our theoretical and experimental results demonstrate the effectiveness of our approach under various scenarios. These findings highlight the potential risk of implementing probabilistic copyright protection in practical applications of text-to-image generative models. Code is available at https://github.com/South7X/VA3.
Related papers
- Copyright-Protected Language Generation via Adaptive Model Fusion [15.48692649098646]
Copyright-Protecting Model Fusion (CP-Fuse) is a novel approach that combines models trained on disjoint sets of copyrighted material during inference.
We show that CP-Fuse significantly reduces the reproduction of protected material without compromising the quality of text and code generation.
arXiv Detail & Related papers (2024-12-09T16:13:17Z) - CopyrightShield: Spatial Similarity Guided Backdoor Defense against Copyright Infringement in Diffusion Models [61.06621533874629]
diffusion model is a prime target for copyright infringement attacks.
This paper provides an in-depth analysis of the spatial similarity of replication in diffusion model.
We propose a novel defense method specifically targeting copyright infringement attacks.
arXiv Detail & Related papers (2024-12-02T14:19:44Z) - CopyrightMeter: Revisiting Copyright Protection in Text-to-image Models [30.618794027527695]
We develop CopyrightMeter, a unified evaluation framework that incorporates 17 state-of-the-art protections and 16 representative attacks.
Our analysis reveals several key findings: (i) most protections (16/17) are not resilient against attacks; (ii) the "best" protection varies depending on the target priority; (iii) more advanced attacks significantly promote the upgrading of protections.
arXiv Detail & Related papers (2024-11-20T09:19:10Z) - Probabilistic Analysis of Copyright Disputes and Generative AI Safety [0.0]
This paper presents a probabilistic approach to analyzing copyright infringement disputes.
The usefulness of this approach is showcased through its application to the inverse ratio rule''
arXiv Detail & Related papers (2024-10-01T08:05:19Z) - Evaluating Copyright Takedown Methods for Language Models [100.38129820325497]
Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material.
This paper introduces the first evaluation of the feasibility and side effects of copyright takedowns for LMs.
We examine several strategies, including adding system prompts, decoding-time filtering interventions, and unlearning approaches.
arXiv Detail & Related papers (2024-06-26T18:09:46Z) - MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models.
Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs.
Empirical evaluations conducted on different datasets validate the efficacy of our approach.
arXiv Detail & Related papers (2024-06-13T15:55:04Z) - Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models [58.065255696601604]
We use compositional property of diffusion models, which allows to leverage multiple prompts in a single image generation.
We argue that it is essential to consider all possible approaches to image generation with diffusion models that can be employed by an adversary.
arXiv Detail & Related papers (2024-04-21T16:35:16Z) - CPR: Retrieval Augmented Generation for Copyright Protection [101.15323302062562]
We introduce CopyProtected generation with Retrieval (CPR), a new method for RAG with strong copyright protection guarantees.
CPR allows to condition the output of diffusion models on a set of retrieved images.
We prove that CPR satisfies Near Access Freeness (NAF) which bounds the amount of information an attacker may be able to extract from the generated images.
arXiv Detail & Related papers (2024-03-27T18:09:55Z) - Copyright Protection and Accountability of Generative AI:Attack,
Watermarking and Attribution [7.0159295162418385]
We propose an evaluation framework to provide a comprehensive overview of the current state of the copyright protection measures for GANs.
Our findings indicate that the current intellectual property protection methods for input images, model watermarking, and attribution networks are largely satisfactory for a wide range of GANs.
arXiv Detail & Related papers (2023-03-15T06:40:57Z) - Trust but Verify: Assigning Prediction Credibility by Counterfactual
Constrained Learning [123.3472310767721]
Prediction credibility measures are fundamental in statistics and machine learning.
These measures should account for the wide variety of models used in practice.
The framework developed in this work expresses the credibility as a risk-fit trade-off.
arXiv Detail & Related papers (2020-11-24T19:52:38Z)
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.