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VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models

Xiang Li, Qianli Shen, Kenji Kawaguchi

TL;DR

The paper tackles the challenge that probabilistic copyright protection for text-to-image diffusion models may be undermined by persistent, adversarial prompting. It introduces Virtually Assured Amplification Attack (VA3), an online framework where an adversary iteratively selects prompts to amplify infringement probability, with Theorem 1 guaranteeing near-certain success given enough interactions and a positive per-step success bound $\sigma$. A practical Anti-NAF procedure combines adversarial prompt optimization with a loss-balanced objective to defeat CP-\u007fk protections, and the authors implement a bandit-based online prompt-selection strategy to manage exploration and exploitation. Empirical results on Pokemon and LAION-mi datasets show that amplification substantially raises infringement rates under CP-\u007fk, while Anti-NAF notably increases infringing outputs; ablations highlight the importance of balanced objectives and prompt design. The findings stress the need for more robust copyright protections and motivate future work on broader attack models, transferability, and stronger defenses in real-world, black-box settings.

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.

VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models

TL;DR

The paper tackles the challenge that probabilistic copyright protection for text-to-image diffusion models may be undermined by persistent, adversarial prompting. It introduces Virtually Assured Amplification Attack (VA3), an online framework where an adversary iteratively selects prompts to amplify infringement probability, with Theorem 1 guaranteeing near-certain success given enough interactions and a positive per-step success bound . A practical Anti-NAF procedure combines adversarial prompt optimization with a loss-balanced objective to defeat CP-\u007fk protections, and the authors implement a bandit-based online prompt-selection strategy to manage exploration and exploitation. Empirical results on Pokemon and LAION-mi datasets show that amplification substantially raises infringement rates under CP-\u007fk, while Anti-NAF notably increases infringing outputs; ablations highlight the importance of balanced objectives and prompt design. The findings stress the need for more robust copyright protections and motivate future work on broader attack models, transferability, and stronger defenses in real-world, black-box settings.

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.
Paper Structure (30 sections, 4 theorems, 23 equations, 9 figures, 8 tables, 2 algorithms)

This paper contains 30 sections, 4 theorems, 23 equations, 9 figures, 8 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Copyright Issues in Generative Models
  4. Vulnerability of Diffusion Models
  5. Preliminaries
  6. Text-to-Image Diffusion Models
  7. Near Access-Freeness Copyright Protection
  8. Method
  9. Problem Formulation
  10. Virtually Assured Amplification Attack on Probabilistic Copyright Protection
  11. Online Prompt Selection
  12. Anti-NAF: Adversarial Prompt Optimization Against NAF Copyright Protection
  13. Experiments
  14. Experimental Settings
  15. Evaluated Datasets and Models
  16. ...and 15 more sections

Key Result

Theorem 1

Following the notations in alg0, for any $\varepsilon \in (0,1)$, the attack is successful with probability at least $1 - \varepsilon$ if $T > \log_{1-\sigma} \varepsilon$, where $\sigma > 0$ is a strictly positive lower-bound on the success probability shared by every single attack.

Figures (9)

  • Figure 1: Given a copyrighted image $y_C$ and a Text-to-Image (T2I) generative model with probabilistic copyright protection, our proposed virtually assured amplification attack (VA3) significantly amplifies the probability of producing infringing generations with persistent interactions of online adversarial prompt selection.
  • Figure 2: Example outputs given the copyright image in \ref{['fig:demo']} as target (potential infringing images are marked with red boundaries). In (a), using a benign prompt, we observe a high incidence of infringing content from models without copyright protection ("w/o CP-$k$"). In contrast, (b) shows that after applying the copyright protection mechanism ("w/ CP-$k$"), all samples are safe as CP-$k$ rejects all infringing content. In (c), we find that amplification (Amp.) attack with a benign prompt results in limited success. Notably, by amplification attack with an adversarial prompt obtained from our proposed Anti-NAF algorithm, almost all output in (d) are copyright-infringed.
  • Figure 3: Visualization of generated images on different copyright targets. The examples in the first and second rows are selected from POKEMON and LAION-mi respectively. The prompts used to generate output are given below each group of images. Remarkably, the copyright-infringed content generated with Anti-NAF amplification reveals the vulnerability of probabilistic copyright protection CP-$k$.
  • Figure 4: The overall FAR-AR curves. The results show that amplification is strongly effective in amplifying the possibility of infringed output, even with small amplification steps.
  • Figure 5: Distributions of SSCD and CLIP similarity score on all target copyrighted images in two datasets using the original caption as prompts. The distributions of the SSCD score are more clearly bimodal to distinguish between non-infringing and infringing samples.
  • ...and 4 more figures

Theorems & Definitions (7)

  • Theorem 1
  • Definition 1: Local Continuity
  • Theorem 2
  • Theorem 1
  • proof
  • Theorem 2
  • proof