Contract And Conquer: How to Provably Compute Adversarial Examples for a Black-Box Model?
Black-box adversarial attacks are widely used as tools to test the robustness of deep neural networks against malicious perturbations of input data aimed at a specific change in the output of the model. Such methods, although they remain empirically effective, usually do not guarantee that an adversarial example can be found for a particular model. In this paper, we propose Contract And Conquer (CAC), an approach to provably compute adversarial examples for neural networks in a black-box manner. The method is based on knowledge distillation of a black-box model on an expanding distillation dataset and precise contraction of the adversarial example search space. CAC is supported by the tra...
| Authors | Anna Chistyakova, Mikhail Pautov |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 12 March 2026 |
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