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

Firuz Juraev; Eldor Abdukhamidov; Mohammed Abuhamad; Tamer Abuhmed

doi:10.1145/3488932.3527278

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

Firuz Juraev, Eldor Abdukhamidov, Mohammed Abuhamad, Tamer Abuhmed

Loyola University Chicago

Sungkyunkwan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security
Publisher	Association for Computing Machinery, Inc
Pages	1207-1209
Number of pages	3
ISBN (Electronic)	9781450391405
DOIs	https://doi.org/10.1145/3488932.3527278
State	Published - May 30 2022
Event	17th ACM ASIA Conference on Computer and Communications Security 2022, ASIA CCS 2022 - Virtual, Online, Japan Duration: May 30 2022 → Jun 3 2022

Publication series

Name	ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security

Conference

Conference	17th ACM ASIA Conference on Computer and Communications Security 2022, ASIA CCS 2022
Country/Territory	Japan
City	Virtual, Online
Period	5/30/22 → 6/3/22

ASJC Scopus Subject Areas

Computer Networks and Communications
Computer Science Applications
Information Systems
Software

Keywords

adversarial attacks
computer vision
deep learning
defenses

Access to Document

10.1145/3488932.3527278

Cite this

Juraev, F., Abdukhamidov, E., Abuhamad, M., & Abuhmed, T. (2022). Depth, Breadth, and Complexity: Ways to Attack and Defend Deep Learning Models. In ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security (pp. 1207-1209). (ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security). Association for Computing Machinery, Inc. https://doi.org/10.1145/3488932.3527278

Depth, Breadth, and Complexity: Ways to Attack and Defend Deep Learning Models. / Juraev, Firuz; Abdukhamidov, Eldor; Abuhamad, Mohammed et al.
ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security. Association for Computing Machinery, Inc, 2022. p. 1207-1209 (ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Juraev, F, Abdukhamidov, E, Abuhamad, M & Abuhmed, T 2022, Depth, Breadth, and Complexity: Ways to Attack and Defend Deep Learning Models. in ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security. ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security, Association for Computing Machinery, Inc, pp. 1207-1209, 17th ACM ASIA Conference on Computer and Communications Security 2022, ASIA CCS 2022, Virtual, Online, Japan, 5/30/22. https://doi.org/10.1145/3488932.3527278

Juraev F, Abdukhamidov E, Abuhamad M, Abuhmed T. Depth, Breadth, and Complexity: Ways to Attack and Defend Deep Learning Models. In ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security. Association for Computing Machinery, Inc. 2022. p. 1207-1209. (ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security). doi: 10.1145/3488932.3527278

Juraev, Firuz ; Abdukhamidov, Eldor ; Abuhamad, Mohammed et al. / Depth, Breadth, and Complexity : Ways to Attack and Defend Deep Learning Models. ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security. Association for Computing Machinery, Inc, 2022. pp. 1207-1209 (ASIA CCS 2022 - Proceedings of the 2022 ACM Asia Conference on Computer and Communications Security).

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

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this