random search

cleverhans/attack_bundling.py

@@ -223,6 +223,44 @@ def fixed_max_confidence_recipe(sess, model, x, y, nb_classes, eps,
            MaxConfidence(t=1., new_work_goal=new_work_goal)]
   bundle_attacks(sess, model, x, y, attack_configs, goals, report_path)
 
+def random_search_max_confidence_recipe(sess, model, x, y, nb_classes, eps,
+                                        clip_min, clip_max, eps_iter, nb_iter,
+                                        report_path, batch_size=BATCH_SIZE):
+  """Max confidence using random search.
+  TODO: this is kind of messy because it has different arguments than the
+  other recipes.
+
+  References:
+  https://openreview.net/forum?id=H1g0piA9tQ
+
+  This version runs each attack a fixed number of times.
+  It is more exhaustive than `single_run_max_confidence_recipe` but because
+  it uses a fixed budget rather than running indefinitely it is more
+  appropriate for making fair comparisons between two models.
+
+  :param sess: tf.Session
+  :param model: cleverhans.model.Model
+  :param x: numpy array containing clean example inputs to attack
+  :param y: numpy array containing true labels
+  :param nb_classes: int, number of classes
+  :param eps: float, maximum size of perturbation (measured by max norm)
+  :param eps_iter: float, step size for one version of PGD attacks
+    (will also run another version with 25X smaller step size)
+  :param nb_iter: int, number of iterations for one version of PGD attacks
+    (will also run another version with 25X more iterations)
+  :param report_path: str, the path that the report will be saved to.
+  :batch_size: int, the total number of examples to run simultaneously
+  """
+  noise_attack = Noise(model, sess)
+  threat_params = {"eps": eps, "clip_min" : clip_min, "clip_max" : clip_max}
+  noise_attack_config = AttackConfig(noise_attack, threat_params)
+  attack_configs = [noise_attack_config]
+  assert batch_size % num_devices == 0
+  dev_batch_size = batch_size // num_devices
+  new_work_goal = {noise_attack_config: 10000}
+  goals = [MaxConfidence(t=1., new_work_goal=new_work_goal)]
+  bundle_attacks(sess, model, x, y, attack_configs, goals, report_path)
+
 class AttackConfig(object):
   """
   An attack and associated parameters.