Fast Gradient Sign Method

The Fast Gradient Sign Method (FGSM) is a technique used to test the robustness of machine learning models, especially neural networks. It works by slightly altering an input (like an image) in the direction that causes the model to make a mistake, while keeping these changes small enough to be hardly noticeable. This is done quickly and efficiently by using the model’s sensitivity to different input features. Essentially, FGSM nudges the input just enough to confuse the model, helping researchers understand and improve its vulnerability to such attacks.