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About Go To: CS230: Lecture 5 Attacking Networks With Adversarial Examples - Generative Adversarial Networks
This document summarizes a lecture on attacking neural networks with adversarial examples and generative adversarial networks. The lecture discusses how to build adversarial examples that can fool pretrained neural networks into misclassifying images. Specifically, it describes how to craft an input image that is classified as a target class, like iguana, while minimally changing a starting image, like a cat photo. The process involves defining a loss function and optimizing the image pixels to minimize the loss. Generative adversarial networks are also introduced as a method using two neural networks in competition to generate new synthetic images.
Detailed Information
| Author: | ['Sarah Eharot'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 30 |
| Language: | English |
| Format: | |
| Price: | FREE |
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