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SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
Wuxinlin Cheng · Chenhui Deng · Zhiqiang Zhao · Yaohui Cai · Zhiru Zhang · Zhuo Feng

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @

A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model. Our approach, named SPADE, exploits bijective distance mapping between the input/output graphs constructed for approximating the manifolds corresponding to the input/output data. By leveraging the generalized Courant-Fischer theorem, we propose a SPADE score for evaluating the adversarial robustness of a given model, which is proved to be an upper bound of the best Lipschitz constant under the manifold setting. To reveal the most non-robust data samples highly vulnerable to adversarial attacks, we develop a spectral graph embedding procedure leveraging dominant generalized eigenvectors. This embedding step allows assigning each data point a robustness score that can be further harnessed for more effective adversarial training of ML models. Our experiments show promising empirical results for neural networks trained with the MNIST and CIFAR-10 data sets.

Author Information

Wuxinlin Cheng (Stevens Institute of Technology)
Chenhui Deng (Cornell University)
Zhiqiang Zhao (Stevens Institute of Technology)
Yaohui Cai (Cornell University)
Zhiru Zhang (Cornell Univeristy)
Zhuo Feng (Stevens Institute of Technology)

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