A framework for robust deep learning models against adversarial attacks based on a protection layer approach
Deep learning (DL) has demonstrated remarkable achievements in various fields. Nevertheless, DL models encounter significant challenges in detecting and defending against adversarial samples (AEs). These AEs are meticulously crafted by adversaries, introducing imperceptible perturbations to clean da...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/27255/2/0272917012024103253681.PDF http://eprints.utem.edu.my/id/eprint/27255/ https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400453 |
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