Evaluating the Robustness of Neural Networks Against Adversarial Perturbations-Reference-Cited by-同舟云学术

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Evaluating the Robustness of Neural Networks Against Adversarial Perturbations

Published:2023-11-23 Issue: Volume:10 Page:911-915
ISSN:
Container-title:2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI)
language:
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Author:

Bhat C. Rohith¹,Nandhini J.²,S Narayanasamy³,Karthik B.⁴,Lakineni Prasanna Kumar⁵,Taqui Syed Noeman⁶

Affiliation:

1. Saveetha School of Engineering Saveetha, Institute of Medical and Technical Sciences,Department of Computer Science and Engineering,Chennai,Tamilnadu,India

2. Jai Shriram Engineering College,Department of ECE,Tirupur,India

3. J.J College of Engineering and Technology,Department of Computer Science and Engineering,Trichy,India

4. Sona College of Technology,Department of Electrical and Electronics Engineering,Salem,India

5. GITAM School of Technology, GITAM University,Department of Computer Science and Engineering,Visakhapatnam,India

6. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,Department of VLSI Microelectronics,Chennai,Tamil Nadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10420826/10420856/10421282.pdf?arnumber=10421282

Reference15 articles.

1. Two-Stage Hybrid Approach of Deep Learning Networks for Interstitial Lung Disease Classification

2. On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques

3. Object Detection Algorithm Based on Multi-Scaled Convolutional Neural Networks

4. DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification

5. Perceptual Generative Adversarial Networks for Small Object Detection

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