=add bim method for generating adversarail sample

adversarial/advbox/attacks/gradientsign.py

@@ -36,3 +36,38 @@ class GradientSignAttack(Attack):
 
 
 FGSM = GradientSignAttack
+
+
+class IteratorGradientSignAttack(Attack):
+    """
+    This attack was originally implemented by Alexey Kurakin(Google Brain).
+    Paper link: https://arxiv.org/pdf/1607.02533.pdf
+    """
+    def _apply(self, image_label, epsilons=100, steps=10):
+        """
+        Apply the iterative gradient sign attack.
+        Args:
+            image_label(list): The image and label tuple list of one element.
+            epsilons(list|tuple|int): The epsilon (input variation parameter).
+            steps(int): The number of iterator steps.
+        Return:
+            numpy.ndarray: The adversarail sample generated by the algorithm.
+        """
+        assert len(image_label) == 1
+        pre_label = np.argmax(self.model.predict(image_label))
+        gradient = self.model.gradient(image_label)
+        min_, max_ = self.model.bounds()
+
+        if not isinstance(epsilons, Iterable):
+            epsilons = np.linspace(0, 1, num=epsilons + 1)
+
+        for epsilon in epsilons:
+            adv_img = image_label[0][0].reshape(gradient.shape)
+            for _ in range(steps):
+                gradient = self.model.gradient([(adv_img, image_label[0][1])])
+                gradient_sign = np.sign(gradient) * (max_ - min_)
+                adv_img = adv_img + epsilon * gradient_sign
+                adv_img = np.clip(adv_img, min_, max_)
+                adv_label = np.argmax(self.model.predict([(adv_img, 0)]))
+                if pre_label != adv_label:
+                    return adv_img