Universal Adversarial Perturbations
Moosavi-Dezfooli, Seyed-Mohsen, Fawzi, Alhussein, Fawzi, Omar, Frossard, Pascal·2017·2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Cited by (1,005)
Items 1,001–1,005 of 1,005
- Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training SetXiang, Zhen, Miller, David J., Wang, Hang, Kesidis, George · 2021 · Neural Computation
- UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated ImagesQu, Yiting, Shen, Xinyue, Wu, Yixin, Backes, Michael, Zannettou, Savvas, Zhang, Yang · 2025 · Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security
- Demonstration Attack against In-Context Learning for Code IntelligenceGe, Yifei, Sun, Weisong, Lou, Yihang, Fang, Chunrong, Zhang, Yiran, Li, Yiming, Zhang, Xiaofang, Liu, Yang, Zhao, Zhihong, Chen, Zhenyu · 2024 · arXiv
- Universal and Transferable Adversarial Attacks on Aligned Language ModelsZou, Andy, Wang, Zifan, Carlini, Nicholas, Nasr, Milad, Kolter, J. Zico, Fredrikson, Matt · 2023 · arXiv
- Adversarial Examples Are Not Bugs, They Are FeaturesIlyas, Andrew, Santurkar, Shibani, Tsipras, Dimitris, Engstrom, Logan, Tran, Brandon, Madry, Aleksander · 2019 · arXiv