The Fast Gradient Sign Method (FGSM) generates an adversarial example as $x' = x + \epsilon \cdot \mathrm{sign}(\nabla_x L(\theta, x, y))$. Why use the sign of the gradient rather than the gradient itself?

Adversarial Machine Learning

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The Fast Gradient Sign Method (FGSM) generates an adversarial example as x^{'} = x + ϵ \cdot sign (\nabla_{x} L (θ, x, y)) . Why use the sign of the gradient rather than the gradient itself?