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New TooBad framework enables stealthy backdoor attacks on diffusion models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new backdoor attack framework called TooBad, specifically designed for diffusion models. This framework significantly enhances the performance of backdoor attacks by employing a novel trigger optimization technique tailored for diffusion models. TooBad demonstrates high attack success rates with a very low poison rate (0.5%) and minimal training epochs, making it stealthy and efficient while evading current state-of-the-art defenses. AI

IMPACT Highlights critical vulnerabilities in diffusion models, necessitating the development of more robust defenses against stealthy and efficient attacks.

RANK_REASON Academic paper detailing a new method for backdoor attacks on diffusion models.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New TooBad framework enables stealthy backdoor attacks on diffusion models

COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger

    Diffusion models (DMs), despite their impressive capabilities across a wide range of generative tasks, have been shown to be vulnerable to backdoor attacks. However, existing backdoor methods face critical trade-offs among key factors: attack performance, stealthiness, time compl…

  2. arXiv cs.CV TIER_1 English(EN) · Long Bao Le ·

    TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger

    Diffusion models (DMs), despite their impressive capabilities across a wide range of generative tasks, have been shown to be vulnerable to backdoor attacks. However, existing backdoor methods face critical trade-offs among key factors: attack performance, stealthiness, time compl…

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