Akhtar and Mian present a comprehensive survey of attacks and defenses of deep neural networks, specifically in computer vision. Published on ArXiv in January 2018, but probably written prior to August 2017, the survey includes recent attacks and defenses. For example, Table 1 presents an overview of attacks on deep neural networks – categorized by knowledge, target and perturbation measure. The authors also provide a strength measure – in the form of a 1-5 start “rating”. Personally, however, I see this rating critically – many of the attacks have not been studies extensively (across a wide variety of defense mechanisms, tasks and datasets). In comparison to the related survey [1], their overview is slightly less detailed – the attacks, for example are described in less mathematical detail and the categorization in Table 1 is less comprehensive.
Table 1: Overview of the discussed attacks on deep neural networks.
[1] Xiaoyong Yuan, Pan He, Qile Zhu, Rajendra Rana Bhat, Xiaolin Li:
Adversarial Examples: Attacks and Defenses for Deep Learning. CoRR abs/1712.07107 (2017)
Akhtar and Mian present a comprehensive survey of attacks and defenses of deep neural networks, specifically in computer vision. Published on ArXiv in January 2018, but probably written prior to August 2017, the survey includes recent attacks and defenses. For example, Table 1 presents an overview of attacks on deep neural networks – categorized by knowledge, target and perturbation measure. The authors also provide a strength measure – in the form of a 1-5 start “rating”. Personally, however, I see this rating critically – many of the attacks have not been studies extensively (across a wide variety of defense mechanisms, tasks and datasets). In comparison to the related survey [1], their overview is slightly less detailed – the attacks, for example are described in less mathematical detail and the categorization in Table 1 is less comprehensive.
Table 1: Overview of the discussed attacks on deep neural networks.