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About Adversarial Examples: Attacks and Defenses For Deep Learning
1. Deep learning models are being widely applied but have been found vulnerable to adversarial examples - intentionally designed input samples that can easily fool neural networks. 2. Adversarial examples pose risks in safety-critical applications like self-driving cars or malware detection systems. Researchers have generated adversarial examples that manipulate traffic signs or remove segmentation of pedestrians. 3. This paper reviews methods for generating adversarial examples, proposes a taxonomy of these methods, investigates applications of adversarial examples, and explores countermeasures and challenges.
Detailed Information
| Author: | ['Jess'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 20 |
| Language: | English |
| Format: | |
| Price: | FREE |
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