中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks

文献类型：期刊论文

作者Han, Husheng6,7,8; Hu, Xing5,8; Hao, Yifan6; Xu, Kaidi4; Dang, Pucheng6,7,8; Wang, Ying3; Zhao, Yongwei2; Du, Zidong5,8; Guo, Qi; Wang, Yanzhi3
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2024
卷号43期号:1页码:366-379
关键词Object detection Streaming media Optical flow Feature extraction Real-time systems Task analysis Detectors Adversarial patch attack deep learning security domain-specific accelerator hardware/software co-design real time
ISSN号0278-0070
DOI10.1109/TCAD.2023.3305932
英文摘要DNN-based video object detection (VOD) powers autonomous driving and video surveillance industries with rising importance and promising opportunities. However, adversarial patch attack yields huge concern in live vision tasks because of its practicality, feasibility, and powerful attack effectiveness. This work proposes Themis, a software/hardware system to defend against adversarial patches for real-time robust VOD. We observe that adversarial patches exhibit extremely localized superficial feature importance in a small region with nonrobust predictions, and thus propose the adversarial region detection algorithm for adversarial effect elimination. Themis also proposes a systematic design to efficiently support the algorithm by eliminating redundant computations and memory traffics. Experimental results show that the proposed methodology can effectively recover the system from the adversarial attack with negligible hardware overhead.
资助项目National Key Research and Development Program of China
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001129816700018
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/38427]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Hu, Xing
作者单位1.Cambricon Technol, Beijing 100191, Peoples R China
2.Cambricon Technol, Dept Architecture Algorithm, Beijing 100191, Peoples R China
3.Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 02115 USA
4.Drexel Univ, Coll Comp & Informat, Dept Comp Sci, Philadelphia, PA 19104 USA
5.Chinese Acad Sci, Shanghai Innovat Ctr Processor Technol, Beijing 100190, Peoples R China
6.Cambricon Technol, Dept Architecture Algorithm, Beijing 100191, Peoples R China
7.Univ Chinese Acad Sci, Sch Comp Sci, Beijing 100049, Peoples R China
8.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Han, Husheng,Hu, Xing,Hao, Yifan,et al. Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2024,43(1):366-379.
APA Han, Husheng.,Hu, Xing.,Hao, Yifan.,Xu, Kaidi.,Dang, Pucheng.,...&Chen, Tianshi.(2024).Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,43(1),366-379.
MLA Han, Husheng,et al."Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 43.1(2024):366-379.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。