Noisy-Defense Variational Auto-Encoder (ND-VAE): an Adversarial Defense Framework to Eliminate Adversarial Attacks
Published In
2023 Fifth International Conference on Transdisciplinary AI (transai)
Document Type
Citation
Publication Date
2023
Abstract
This paper presents a robust adversarial defense mechanism, Noisy-Defense Variational Auto-Encoder (ND-VAE), that combines the strengths of Nouveau VAE (NVAE) and Defense-VAE to effectively eliminate adversarial attacks from contaminated images. The ND-VAE, trained using noisy images, not only removes adversarial perturbations but also preserves the image details, thereby lowering adversarial training costs. By utilizing advanced NVAE architectures and incorporating a noise filter, the defense system efficiently mitigates both previously seen and unseen adversarial attacks. Our evaluations on both MNIST and Fashion-MNIST datasets confirm the high efficiency of ND-VAE, including better zero-shot performance, demonstrating a balanced approach to model expressivity and noise resistance in image classifiers.11Code available at https://github.com/shayan223/ND-VAE
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DOI
10.1109/TransAI60598.2023.00018
Persistent Identifier
https://archives.pdx.edu/ds/psu/41277
Publisher
IEEE
Citation Details
Jalalipour, S., & Rekabdar, B. (2023, September 25). Noisy-Defense Variational Auto-Encoder (ND-VAE): An Adversarial Defense Framework to Eliminate Adversarial Attacks. 2023 Fifth International Conference on Transdisciplinary AI (TransAI). https://doi.org/10.1109/transai60598.2023.00018