Multi-Spectral Palmprints Joint Attack and Defense With Adversarial Examples Learning

Author:

Zhu Qi1ORCID,Zhou Yuze1ORCID,Fei Lunke2ORCID,Zhang Daoqiang1ORCID,Zhang David3ORCID

Affiliation:

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

3. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Key Research and Development Plan of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Reference45 articles.

1. Explaining and harnessing adversarial examples;goodfellow;arXiv 1412 6572,2014

2. Adversarial machine learning at scale;kurakin;arXiv 1611 01236,2016

3. Intriguing properties of neural networks;szegedy;arXiv 1312 6199,2013

4. Image Quality Assessment: From Error Visibility to Structural Similarity

5. Towards Transferable Adversarial Attack Against Deep Face Recognition

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1. Efficient Contactless Palmprint Recognition System Based on Deep Rule-Based Classification;Acta Informatica Pragensia;2024-08-04

2. Learning to Generalize Unseen Dataset for Cross-Dataset Palmprint Recognition;IEEE Transactions on Information Forensics and Security;2024

3. Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization;IEEE Transactions on Information Forensics and Security;2024

4. Adversarial Detection and Fusion Method for Multispectral Palmprint Recognition;International Journal of Image and Graphics;2023-11-01

5. Comprehensive Competition Mechanism in Palmprint Recognition;IEEE Transactions on Information Forensics and Security;2023

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