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New PEPPER defense combats backdoor attacks in text-to-image models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new defense mechanism called PEPPER (PErcePtion-Guided Perturbation) to combat backdoor attacks in text-to-image diffusion models. These attacks can manipulate model outputs towards harmful content by embedding triggers in prompts. PEPPER works by rewriting input captions to be visually similar but semantically distant from the original, thereby disrupting the embedded trigger without requiring model retraining or access to weights. This method has shown particular effectiveness against text encoder-based attacks, improving robustness and maintaining generation quality, and can be combined with other defenses for enhanced results. AI

IMPACT Enhances the security and reliability of text-to-image generation models against malicious manipulation.

RANK_REASON The cluster contains an academic paper detailing a new method for defending against specific types of attacks on AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PEPPER defense combats backdoor attacks in text-to-image models

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The cluster contains an academic paper detailing a new method for defending against specific types of attacks on AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Oscar Chew, Po-Yi Lu, Jayden Lin, Kuan-Hao Huang, Hsuan-Tien Lin ·

    PEPPER: Perception-Guided Perturbation for Robust Backdoor Defense in Text-to-Image Diffusion Models

    arXiv:2511.16830v4 Announce Type: replace Abstract: Recent studies show that text-to-image (T2I) diffusion models are vulnerable to backdoor attacks, where a trigger in the input prompt can steer generation toward harmful or unintended content. Beyond the trigger token itself, ba…

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