REFINE: Inversion-Free Backdoor Defense via Model Reprogramming

ICLR 2025 Conference Submission4208 Authors

Keywords: Backdoor Defense, Model Reprogramming, Backdoor Attack, AI Security

Abstract: Backdoor attacks on deep neural networks (DNNs) have emerged as a significant security threat, allowing adversaries to implant hidden malicious behaviors during the model training phase. Pre-processing-based defense, which is one of the most important defense paradigms, typically focuses on input transformations or backdoor trigger inversion (BTI) to deactivate or eliminate embedded backdoor triggers during the inference process. However, these methods suffer from inherent limitations: transformation-based defenses often struggle to balance the intensity of transformations with preserving the model's accuracy, while BTI-based defenses require accurate reconstruction of the trigger patterns, which is rarely achievable without prior knowledge. In this paper, we propose REFINE, an inversion-free backdoor defense method based on model reprogramming. REFINE consists of two key components: (1) an input transformation module that disrupts both benign and backdoor patterns, generating new benign features; and (2) an output remapping module that redefines the model's output domain to guide the input transformations effectively. By further integrating supervised contrastive loss, REFINE enhances the defense capabilities while maintaining model utility. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our REFINE and its resistance to potential adaptive attacks.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 4208