Defending Against Patch-Based and Texture-Based Adversarial Attacks With Spectral Decomposition

Summary

Researchers propose Adversarial Spectrum Defense (ASD), which uses Discrete Wavelet Transform (DWT) spectral decomposition to analyze adversarial patterns across multiple frequency scales. Combined with off-the-shelf Adversarial Training (AT), ASD+AT reportedly achieves state-of-the-art performance against patch-based and texture-based attacks, outperforming previous defenses by 21.73% in the AP metric, including against adaptive adversaries designed against ASD.

Topics

• Adversarial machine learning

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