Merge pull request #46 from openai/other_classes

created helper function other_classes

Author: npapernot

cleverhans/attacks.py

@@ -11,6 +11,7 @@
 import multiprocessing as mp
 
 from . import utils_tf
+from . import utils
 
 from tensorflow.python.platform import flags
 FLAGS = flags.FLAGS
@@ -200,8 +201,7 @@ def jacobian(sess, x, grads, target, X):
 
     # Sum over all classes different from the target class to prepare for
     # saliency map computation in the next step of the attack
-    other_classes = list(xrange(FLAGS.nb_classes))
-    other_classes.remove(target)
+    other_classes = utils.other_classes(FLAGS.nb_classes, target)
     grad_others = np.sum(jacobian_val[other_classes, :, :], axis=0)
 
     return jacobian_val[target], grad_others

cleverhans/utils.py

@@ -79,3 +79,17 @@ def batch_indices(batch_nb, data_length, batch_size):
         end -= shift
 
     return start, end
+
+
+def other_classes(nb_classes, class_ind):
+    """
+    Heper function that returns a list of class indices without one class
+    :param nb_classes: number of classes in total
+    :param class_ind: the class index to be omitted
+    :return: list of class indices without one class
+    """
+
+    other_classes_list = list(xrange(nb_classes))
+    other_classes_list.remove(class_ind)
+
+    return other_classes_list

tutorials/mnist_tutorial_jsma.py

@@ -14,6 +14,7 @@
 from cleverhans.utils_mnist import data_mnist, model_mnist
 from cleverhans.utils_tf import tf_model_train, tf_model_eval
 from cleverhans.attacks import jsma, jacobian_graph
+from cleverhans.utils import other_classes
 
 FLAGS = flags.FLAGS
 
@@ -101,8 +102,7 @@ def main(argv=None):
     for sample_ind in xrange(FLAGS.source_samples):
         # We want to find an adversarial example for each possible target class
         # (i.e. all classes that differ from the label given in the dataset)
-        target_classes = list(xrange(FLAGS.nb_classes))
-        target_classes.remove(int(np.argmax(Y_test[sample_ind])))
+        target_classes = other_classes(FLAGS.nb_classes, int(np.argmax(Y_test[sample_ind])))
 
         # Loop over all target classes
         for target in target_classes:
@@ -128,5 +128,8 @@ def main(argv=None):
     percentage_perturbed = np.mean(perturbations)
     print('Avg. rate of perterbed features {0}'.format(percentage_perturbed))
 
+    # Close TF session
+    sess.close()
+
 if __name__ == '__main__':
     app.run()