Target-X: An Efficient Algorithm for Generating Targeted Adversarial Images to Fool Neural Networks-Reference-Cited by-同舟云学术

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Target-X: An Efficient Algorithm for Generating Targeted Adversarial Images to Fool Neural Networks

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)
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Author:

Khamaiseh Samer Y.¹,Bagagem Derek²,Al-Alaj Abdullah³,Mancino Mathew⁴,Alomari Hakem¹,Aleroud Ahmed⁵

Affiliation:

1. Miami University,Dept. of Computer Science & Software Eng.,Ohio,USA

2. Boise State University,Deptartment of Computer Science,Idaho,USA

3. Virginia Wesleyan University,Department of Computer Science,Virginia,USA

4. CACI International,New Jersey,USA

5. Augusta University,School of Computer and Cyber Sciences,Georgia,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10196792/10196808/10197071.pdf?arnumber=10197071

Reference37 articles.

1. Enhancing Adversarial Example Transferability With an Intermediate Level Attack

2. Densely Connected Convolutional Networks

3. Medical Image Classification Using Deep Learning

4. Detecting Saturation Attacks in SDN via Machine Learning

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DeepCNN: A Dual Approach to Fault Localization and Repair in Convolutional Neural Networks;IEEE Access;2024

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