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About (DownSub - Com) Stanford CS230 - Deep Learning - Autumn 2018 - Lecture 4 - Adversarial Attacks - GANs
The document discusses adversarial attacks on neural networks. Specifically, it describes how to generate an adversarial example that fools a pre-trained image classification network into misclassifying an image. The goal is to find an input image x that does not look like an iguana, but when run through the network, is classified as an iguana. This is done by defining a loss function measuring the difference between the network's output and the target class (iguana), and using gradient descent to minimize the loss by updating the image pixels, without changing the network parameters. However, there is no guarantee the forged image x will actually look like an iguana.
Detailed Information
| Author: | ['yassine'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 21 |
| Language: | English |
| Format: | |
| Price: | FREE |
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