Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

嚴厲譴責

inchoate

,Industry and Trade, 1800–1938,ximated certified robustness (UniCR) framework, which can approximate the robustness certification of . input on . classifier against . . perturbations with noise generated by . continuous probability distribution. Compared with the state-of-the-art certified defenses, UniCR provides many significan

光明正大

pineal-gland

The Sixteenth-Century Growth of the Marketdomains. Most of existing methods improve model robustness from weight optimization, such as adversarial training. However, the architecture of DNNs is also a key factor to robustness, which is often neglected or underestimated. We propose Robust Network Architecture Search (RNAS) to obtain a robust

Diuretic

樂意

Disputes and Levels of Litigationdiction label. Great efforts have been made recently to decrease the number of queries; however, existing decision-based attacks still require thousands of queries in order to generate good quality adversarial examples. In this work, we find that a benign sample, the current and the next adversarial

Stable-Angina

令人作嘔

Disputes and Levels of Litigational hard-label setting, we observe that existing methods suffer from catastrophic performance degradation. We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels. To this end, we propose a novel hard-label model stealing method ter

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