Orthogonal Deep Models as Defense Against Black-Box Attacks
Deep learning has demonstrated state-of-the-art performance for a variety of challenging computer vision tasks. On one hand, this has enabled deep visual models to pave the way for a plethora of critical applications like disease prognostics and smart surveillance. On the other, deep learning has al...
Main Authors: | Mohammad A. A. K. Jalwana, Naveed Akhtar, Mohammed Bennamoun, Ajmal Mian |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9129688/ |
Similar Items
-
Advances in Adversarial Attacks and Defenses in Computer Vision: A Survey
by: Naveed Akhtar, et al.
Published: (2021-01-01) -
Adversarial attacks and defenses in deep learning
by: LIU Ximeng, et al.
Published: (2020-10-01) -
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
by: Naveed Akhtar, et al.
Published: (2018-01-01) -
Adversarial Deep Learning: A Survey on Adversarial Attacks and Defense Mechanisms on Image Classification
by: Samer Y. Khamaiseh, et al.
Published: (2022-01-01) -
Besting the Black-Box: Barrier Zones for Adversarial Example Defense
by: Kaleel Mahmood, et al.
Published: (2022-01-01)