Robustness and Transferability of Adversarial Attacks on Different Image Classification Neural Networks
Recent works demonstrated that imperceptible perturbations to input data, known as adversarial examples, can mislead neural networks’ output. Moreover, the same adversarial sample can be transferable and used to fool different neural models. Such vulnerabilities impede the use of neural networks in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/3/592 |