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Author

Jiahao Yang

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Open access Dec 2024

Adversarial Purification by Consistency-Aware Latent Space Optimization on Data Manifolds

Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predictions. Adversarial purification has been an effective means to improve DNNs robustness by removing these perturbations before feeding the da...

Shu-Hai Zhang, Jia-Hao Yang, Hui Luo et al. · 0 citations
Open access Aug 2026

Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds.

This paper reveals that samples generated by a well-trained generative model are close to clean ones but far from adversarial ones, and proposes Consistency Model-based Adversarial Purification (CMAP), which optimizes vectors within the latent space of a pre-trained consistency model to generate samples for restoring c...

Shuhai Zhang, Jiahao Yang, Hui Luo et al. · 0 citations

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