D etect S ec : Evaluating the robustness of object detection models to adversarial attacks

Author:

Du Tianyu1,Ji Shouling12,Wang Bo1,He Sirui1,Li Jinfeng1,Li Bo3,Wei Tao4,Jia Yunhan5,Beyah Raheem6,Wang Ting7

Affiliation:

1. Institute of Computer Science and Technology Zhejiang University Hangzhou China

2. Department of Information Science Binjiang Institute of Zhejiang University Hangzhou China

3. Department of Computer Science University of Illinois at Urbana‐Champaign Urbana‐Champaign Illinois USA

4. Department of Foundational Security Ant Group Hangzhou China

5. Department of Security and Privacy Technology ByteDance AI Lab Palo Alto California USA

6. Institute of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA

7. Department of Information Sciences and Technology The Pennsylvania State University State College Pennsylvania USA

Publisher

Hindawi Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Reference70 articles.

1. KarpathyA Fei‐FeiL. Deep visual‐semantic alignments for generating image descriptions.2015:3128‐3137.

2. GirshickR. Fast R‐CNN.2015:1440‐1448.

3. RenS HeK GirshickR SunJ. Faster R‐CNN: towards real‐time object detection with region proposal networks.2015.

4. SzegedyC. Intriguing properties of neural networks.2014.

5. GoodfellowIJ ShlensJ SzegedyC. Explaining and harnessing adversarial examples.2015.

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