Adversarial Examples Generation for Deep Product Quantization Networks on Image Retrieval

Author:

Chen Bin1ORCID,Feng Yan2,Dai Tao3ORCID,Bai Jiawang2ORCID,Jiang Yong2ORCID,Xia Shu-Tao2ORCID,Wang Xuan1ORCID

Affiliation:

1. Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China

2. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China

3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

Funder

National Natural Science Foundation of China

Shenzhen Basic Research Project of China

National Science and Technology

PCNL KEY project

Basic and Applied Basic Research Foundation of Guangdong Province

Research and Development Plan of Key Fields in Guangdong Province

Shenzhen Foundational Research Funding

PINGAN-HITsz Intelligence Finance Research Center

Ricoh-HITsz Joint Research Center

GBase-HITsz Joint Research Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Reference58 articles.

1. Universal Perturbation Attack Against Image Retrieval

2. Adversarial risk and the dangers of evaluating against weak attacks;uesato,2018

3. Intriguing properties of neural networks;szegedy;Proc Int Conf Learn Representations,2014

4. Black-box adversarial attacks with limited queries and information;ilyas,2018

5. Adversarial Examples for Hamming Space Search

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