c9949c1bf26f2a18de24176bb5ce9778834bcdd2,art/attacks/evasion/adversarial_patch/adversarial_patch.py,AdversarialPatch,__init__,#AdversarialPatch#Any#Any#Any#Any#Any#Any#Any#Any#Any#,55

Before Change

        if not isinstance(classifier, ClassifierNeuralNetwork) or not isinstance(classifier, ClassifierGradients):
            raise ClassifierError(self.__class__, [ClassifierNeuralNetwork, ClassifierGradients], classifier)

        kwargs = {
            "target": target,
            "rotation_max": rotation_max,
            "scale_min": scale_min,
            "scale_max": scale_max,
            "learning_rate": learning_rate,
            "max_iter": max_iter,
            "batch_size": batch_size,
            "clip_patch": clip_patch,
        }
        self.set_params(**kwargs)
        self.patch = None

    def generate(self, x, y=None, **kwargs):
        
        Generate adversarial samples and return them in an array.

After Change

        if not isinstance(classifier, ClassifierNeuralNetwork) or not isinstance(classifier, ClassifierGradients):
            raise ClassifierError(self.__class__, [ClassifierNeuralNetwork, ClassifierGradients], classifier)

        if isinstance(self.classifier, TensorFlowV2Classifier):
            self._attack = AdversarialPatchTensorFlow(classifier=classifier, target=target, rotation_max=rotation_max,
                                                      scale_min=scale_min, scale_max=scale_max,
                                                      learning_rate=learning_rate, max_iter=max_iter,
                                                      clip_patch=clip_patch, batch_size=batch_size)
        else:
            self._attack = AdversarialPatchNumpy(classifier=classifier, target=target, rotation_max=rotation_max,
                                                 scale_min=scale_min, scale_max=scale_max, learning_rate=learning_rate,
                                                 max_iter=max_iter, clip_patch=clip_patch, batch_size=batch_size)

    def generate(self, x, y=None, **kwargs):
        
        Generate adversarial samples and return them in an array.

Instances