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Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning

Machine Learning 2020-10-02 v1 Machine Learning

Abstract

Stochastic Activation Pruning (SAP) (Dhillon et al., 2018) is a defense to adversarial examples that was attacked and found to be broken by the "Obfuscated Gradients" paper (Athalye et al., 2018). We discover a flaw in the re-implementation that artificially weakens SAP. When SAP is applied properly, the proposed attack is not effective. However, we show that a new use of the BPDA attack technique can still reduce the accuracy of SAP to 0.1%.

Cite

@article{arxiv.2010.00071,
  title  = {Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning},
  author = {Guneet S. Dhillon and Nicholas Carlini},
  journal= {arXiv preprint arXiv:2010.00071},
  year   = {2020}
}
R2 v1 2026-06-23T18:55:14.394Z