WPEBA: A Novel Ensemble Black-Box Adversarial Attack for Visual Recognition Systems via Wavelet Packet Decomposition

Summary

WPEBA is a frequency-driven ensemble black-box adversarial attack for visual recognition systems that uses wavelet packet decomposition to split images into frequency sub-bands. It adjusts sub-band weights from internal gradient feedback and updates surrogate-model weights from target-model query feedback, reaching an average attack success rate of nearly 99% with one or two queries across six standard architectures. The authors report it also remains effective against defended models and the Google Cloud Vision API.

Topics

• Adversarial machine learning

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