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About The Limitations of Deep Learning in Adversarial Settings
This paper proposes new algorithms for crafting adversarial examples against deep neural networks. The algorithms directly map inputs to outputs to achieve an adversarial goal, such as causing a misclassification, while minimizing changes to the inputs. The authors tested their algorithms on computer vision tasks and found they could reliably produce misclassifications with only small changes averaging 4% of input features per image. They also analyzed the vulnerability of different types of inputs to adversarial perturbations. In summary, the paper introduces new effective methods for generating adversarial examples against deep learning models and studies their properties.
Detailed Information
| Author: | ['ZIX326'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 16 |
| Language: | English |
| Format: | |
| Price: | FREE |
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