"Depth, Breadth, and Complexity: Ways to Attack and Defend Deep Learning ..."

Firuz Juraev et al. (2022)

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DOI: 10.1145/3488932.3527278

access: closed

type: Conference or Workshop Paper

metadata version: 2022-10-02

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Firuz Juraev, Eldor Abdukhamidov, Mohammed Abuhamad, Tamer Abuhmed:
Depth, Breadth, and Complexity: Ways to Attack and Defend Deep Learning Models. AsiaCCS 2022: 1207-1209

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