705d7ff759a0b76ddf1b30ab8ef135c0b67efa8b,art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py,ProjectedGradientDescentPytorch,generate,#ProjectedGradientDescentPytorch#Any#Any#,123

Before Change

        //     else 100 * compute_success(self.classifier, x, y, adv_x_best, self.targeted, batch_size=self.batch_size),
        // )

        return adv_x.cpu().detach().numpy()

    def _compute_perturbation(self, batch, batch_labels):
        // Pick a small scalar to avoid division by 0
        tol = 10e-8

After Change

        mask = kwargs.get("mask")
        if mask is not None:
            // Ensure the mask is broadcastable
            if len(mask.shape) > len(x.shape) or mask.shape != x.shape[-len(mask.shape):]:
                raise ValueError("Mask shape must be broadcastable to input shape.")

        adv_x_best = None

Instances