Temporal shuffling for defending deep action recognition models against adversarial attacks

Author:

Hwang JaehuiORCID,Zhang Huan,Choi Jun-Ho,Hsieh Cho-Jui,Lee Jong-SeokORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference36 articles.

1. Anand, A. P., Gokul, H., Srinivasan, H., Vijay, P., & Vijayaraghavan, V. (2020). Adversarial patch defense for optical flow networks in video action recognition. In Proceedings of the IEEE international conference on machine learning and application.

2. Athalye, A., Carlini, N., & Wagner, D. (2018). Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In Proceedings of the international conference on machine learning.

3. Athalye, A., Engstrom, L., Ilyas, A., & Kwok, K. (2018). Synthesizing robust adversarial examples. In Proceedings of the international conference on machine learning.

4. Deep convolutional networks do not classify based on global object shape;Baker;PLoS Computational Biology,2018

5. Bertasius, G., Wang, H., & Torresani, L. (2021). Is space-time attention all you need for video understanding?. In Proceedings of the international conference on machine learning.

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