Exploring the Adversarial Robustness of Video Object Segmentation via One-shot Adversarial Attacks
Author:
Affiliation:
1. Fudan University, Shanghai, China
Funder
Scientific and Technological Innovation Action Plan of Shanghai Science and Technology Committee
National Key R&D Program of China
National Natural Science Foundation of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3581783.3611827
Reference60 articles.
1. Learning What to Learn for Video Object Segmentation
2. One-Shot Video Object Segmentation
3. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57. Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57.
4. One-Shot Adversarial Attacks on Visual Tracking With Dual Attention
5. Zhaoyu Chen , Bo Li , Shuang Wu , Shouhong Ding , and Wenqiang Zhang . 2023 a. Query-Efficient Decision-based Black-Box Patch Attack. arXiv preprint arXiv:2307.00477 ( 2023 ). Zhaoyu Chen, Bo Li, Shuang Wu, Shouhong Ding, and Wenqiang Zhang. 2023 a. Query-Efficient Decision-based Black-Box Patch Attack. arXiv preprint arXiv:2307.00477 (2023).
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