A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach-Reference-Cited by-同舟云学术

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A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach

Published:2024 Issue: Volume:12 Page:17522-17540
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Al-Andoli Mohammed Nasser¹^ORCID,Tan Shing Chiang²^ORCID,Sim Kok Swee³^ORCID,Goh Pey Yun²^ORCID,Lim Chee Peng⁴^ORCID

Affiliation:

1. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

2. Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia

3. Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia

4. Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, Australia

Funder

Fundamental Research Grant Scheme (FRGS) from the Malaysia Ministry of Higher Education

Multimedia University’s

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10400453.pdf?arnumber=10400453

Reference42 articles.

1. Deep learning models-based CT-scan image classification for automated screening of COVID-19

2. Prediction and detection of potholes in urban roads: Machine learning and deep learning based image segmentation approaches

3. Parallel Deep Learning with a hybrid BP-PSO framework for feature extraction and malware classification

4. An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection

5. Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle

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1. The Impact of Simultaneous Adversarial Attacks on Robustness of Medical Image Analysis;IEEE Access;2024

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