PANDA: Diffusion-Guided Purification and Adaptation for Robust Point Cloud Classification Against Adversarial Attack
Summary
Deep learning models that classify point clouds (3D data made of many points in space) are vulnerable to adversarial attacks (carefully crafted inputs designed to fool AI systems). This paper proposes PANDA, a two-stage defense that uses diffusion models (neural networks that gradually transform noisy data into clean data) to purify attacked data and then retrains the classifier to work well with the purified data, addressing limitations in existing diffusion-based defenses.
Solution / Mitigation
PANDA combines two components: PANDA-P, which uses a dual-branch diffusion training strategy that optimizes on both clean-to-clean and adversarial-to-clean paths to improve purification, and PANDA-A, which fine-tunes the classifier using a consistency-driven learning objective to reshape the classifier's feature space and recalibrate decision boundaries for the purified data.
Classification
Related Issues
Original source: http://ieeexplore.ieee.org/document/11612940
First tracked: August 13, 2026 at 08:05 PM
Classified by LLM (prompt v3) · confidence: 85%