On the Adversarial Robustness of Deep Learning

Опубликовано 7 декабря 2022, 2:43

Research Talk
Jun Zhu, Tsinghua University 

Although deep learning methods have obtained significant progress in many tasks, it has been widely recognized that the current methods are vulnerable to adversarial noise. This weakness poses serious risk to safety-critical applications. In this talk, I will present some recent progress on adversarial attack and defense for deep learning, including theory, algorithms and benchmarks.

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