Real-Time Adversarial Attacks
Published in Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019
Recommended citation: Gong, Yuan, Boyang Li, Christian Poellabauer, and Yiyu Shi. "Real-time adversarial attacks." In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4672-4680. AAAI Press, 2019. https://www.ijcai.org/Proceedings/2019/0649.pdf
In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail. While these attack methods are very effective, they only focus on scenarios where the target model takes static input, i.e., an attacker can observe the entire original sample and then add a perturbation at any point of the sample. These attack approaches are not applicable to situations where the target model takes streaming input, i.e., an attacker is only able to observe past data points and add perturbations to the remaining (unobserved) data points of the input. In this paper, we propose a real-time adversarial attack scheme for machine learning models with streaming inputs.
BibTex: @inproceedings{gong2019real, title={Real-time adversarial attacks}, author={Gong, Yuan and Li, Boyang and Poellabauer, Christian and Shi, Yiyu}, booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence}, pages={4672–4680}, year={2019}, organization={AAAI Press} }