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VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models (2306.06874v5)

Published 12 Jun 2023 in cs.CR, cs.CV, and cs.LG

Abstract: Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising. They are the backbone of many generative AI applications, such as text-to-image conditional generation. However, recent studies have shown that basic unconditional DMs (e.g., DDPM and DDIM) are vulnerable to backdoor injection, a type of output manipulation attack triggered by a maliciously embedded pattern at model input. This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis for DMs. Our framework covers mainstream unconditional and conditional DMs (denoising-based and score-based) and various training-free samplers for holistic evaluations. Experiments show that our unified framework facilitates the backdoor analysis of different DM configurations and provides new insights into caption-based backdoor attacks on DMs. Our code is available on GitHub: \url{https://github.com/IBM/villandiffusion}

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Authors (3)
  1. Sheng-Yen Chou (4 papers)
  2. Pin-Yu Chen (311 papers)
  3. Tsung-Yi Ho (57 papers)
Citations (34)
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