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Svetlana Karslioglu
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Update fgsm_tutorial.py (#2082)
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beginner_source/fgsm_tutorial.py

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machine learning. You may be surprised to find that adding imperceptible
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perturbations to an image *can* cause drastically different model
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performance. Given that this is a tutorial, we will explore the topic
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via example on an image classifier. Specifically we will use one of the
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via example on an image classifier. Specifically, we will use one of the
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first and most popular attack methods, the Fast Gradient Sign Attack
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(FGSM), to fool an MNIST classifier.
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