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Oct 13, 2022
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Update fgsm_tutorial.py #2082

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2 changes: 1 addition & 1 deletion beginner_source/fgsm_tutorial.py
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Expand Up @@ -17,7 +17,7 @@
machine learning. You may be surprised to find that adding imperceptible
perturbations to an image *can* cause drastically different model
performance. Given that this is a tutorial, we will explore the topic
via example on an image classifier. Specifically we will use one of the
via example on an image classifier. Specifically, we will use one of the
first and most popular attack methods, the Fast Gradient Sign Attack
(FGSM), to fool an MNIST classifier.

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