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About Deeplearning2015 Goodfellow Adversarial Examples 01
This presentation discusses adversarial examples in deep learning models. It covers how neural networks can be fooled by small perturbations to inputs and how this poses security risks. The presentation explores different types of adversarial attacks and defenses. It is shown that adversarial training, which involves training on perturbed examples, is an effective defense that can expand the region of input space where models are robust. The presenter advocates for using adversarial examples as an evaluation benchmark and warns against claims that defenses will work without rigorous testing on such examples.
Detailed Information
| Author: | ['gagamamapapa'] |
|---|---|
| Publication Year: | 2015 |
| Pages: | 43 |
| Language: | English |
| Format: | |
| Price: | FREE |
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