DBLP: Noise Bridge Consistency Distillation For Efficient And Reliable Adversarial Purification

Chihan Huang; Belal Alsinglawi; Islam Al-qudah

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arXiv · 2508.00552

DBLP: Noise Bridge Consistency Distillation For Efficient And Reliable Adversarial Purification

Chihan Huang·Belal Alsinglawi·Islam Al-qudah

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Abstract

Recent advances in deep neural networks (DNNs) have led to remarkable success across a wide range of tasks. However, their susceptibility to adversarial perturbations remains a critical vulnerability. Existing diffusion-based adversarial purification methods often require intensive iterative denoising, severely limiting their practical deployment. In this paper, we propose Diffusion Bridge Distillation for Purification (DBLP), a novel and efficient diffusion-based framework for adversarial purification. Central to our approach is a new objective, noise bridge distillation, which constructs a principled alignment between the adversarial noise distribution and the clean data distribution within a latent consistency model (LCM). To further enhance semantic fidelity, we introduce adaptive semantic enhancement, which fuses multi-scale pyramid edge maps as conditioning input to guide the purification process. Extensive experiments across multiple datasets demonstrate that DBLP achieves state-of-the-art (SOTA) robust accuracy, superior image quality, and around 0.2s inference time, marking a significant step toward real-time adversarial purification.

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Chihan Huang, Belal Alsinglawi, Islam Al-qudah. 2025-08-01. DBLP: Noise Bridge Consistency Distillation For Efficient And Reliable Adversarial Purification. https://arxiv.org/abs/2508.00552

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