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InfoResearchPreprint

SemPurify: Semantics-Aware Data Purification for Black-box Backdoor Defense

Source
Arxiv (cs.RO + cs.CV security)(opens in a new tab)
Published
Record updated
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Summary

SemPurify is a data purification framework for black-box backdoor defense that uses multi-level semantics, including low-level semantic embeddings and high-level image tags, to guide a text-to-image model during image restoration. The authors state that the approach removes backdoors across classification models, CLIP-style encoders and generative models while preserving semantic information for downstream tasks. The paper argues that existing purification defenses that rely on transformations such as blurring degrade image quality and can alter visual or semantic content.

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