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浏览/检索结果: 共6条，第1-6条 帮助 限定条件 发表日期：2020     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 FCDM: A Methodology Based on Sensor Pattern Noise Fingerprinting for Fast Confidence Detection to Adversarial Attacks 期刊论文 IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2020, 卷号: 39, 期号: 12, 页码: 4791-4804 作者:  Lan, Yazhu;  Nixon, Kent W.;  Guo, Qingli;  Zhang, Guohe;  Xu, Yuanchao 收藏  |  浏览/下载：36/0  |  提交时间：2021/12/01 Perturbation methods  Computational modeling  Data integrity  Detectors  Optimization  Field programmable gate arrays  Hardware  Adversarial attacks  confidence detection  deep neural networks (DNNs)  FPGA-based hardware architecture  sensor pattern noise (SPN)   Robust Image Classification with Cognitive-Driven Color Priors 期刊论文 ELECTRONICS, 2020, 卷号: 9, 期号: 11, 页码: 16 作者:  Gu, Peng;  Zhu, Chengfei;  Lan, Xiaosong;  Wang, Jie;  Li, Shuxiao 收藏  |  浏览/下载：37/0  |  提交时间：2021/01/06 adversarial samples  color prior model  image classifification   Efficient Joint Gradient Based Atack Against SOR Defense for 3D Point Cloud Classification 会议论文 Virtual, October 12–16, 2020 作者:  Chengcheng Ma;  Weiliang Meng;  Baoyuan Wu;  Shibiao Xu;  Xiaopeng Zhang 收藏  |  浏览/下载：21/0  |  提交时间：2022/04/02 Boosting Decision-based Black-box Adversarial Attacks with Random Sign Flip 会议论文 UK, 2020-8 作者:  Weilun, Chen;  Zhaoxiang, Zhang;  Xiaolin, Hu;  Baoyuan, Wu 收藏  |  浏览/下载：19/0  |  提交时间：2021/05/28 Adversarial attacks on Faster R-CNN object detector 期刊论文 NEUROCOMPUTING, 2020, 卷号: 382, 期号: 无, 页码: 87-95 作者:  Wang, Yutong;  Wang, Kunfeng;  Zhu, Zhanxing;  Wang, Fei-Yue 收藏  |  浏览/下载：16/0  |  提交时间：2020/03/30 Adversarial attack  Object detection  White-box attack  Black-box attack   Adversarial Attacks and Defenses in Images, Graphs and Text: A Review 期刊论文 International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 2, 页码: 151-178 作者:  Han Xu;  Yao Ma;  Hao-Chen Liu;  Debayan Deb;  Hui Liu 收藏  |  浏览/下载：15/0  |  提交时间：2021/02/22 Adversarial example  model safety  robustness  defenses  deep learning.