def save(criteria, report, report_path, adv_x_val):
"""
Saves the report and adversarial examples.
:param criteria: dict, of the form returned by AttackGoal.get_criteria
:param report: dict containing a confidence report
:param report_path: string, filepath
:param adv_x_val: numpy array containing dataset of adversarial examples
"""
print_stats(criteria['correctness'], criteria['confidence'], 'bundled')
serial.save(report_path, report)
assert report_path.endswith(".joblib")
adv_x_path = report_path[:-len(".joblib")] + "_adv.npy"
np.save(adv_x_path, adv_x_val)
def run_batch_with_goal(sess, model, x, y, adv_x_val, criteria, attack_configs,
run_counts, goal, report, report_path):
"""
Runs attack bundling on one batch of data.
This function is mostly intended to be called by
`bundle_attacks_with_goal`.
:param sess: tf.session.Session
:param model: cleverhans.model.Model
:param x: numpy array containing clean example inputs to attack
:param y: numpy array containing true labels
:param adv_x_val: numpy array containing the adversarial examples made so far
by earlier work in the bundling process
:param criteria: dict mapping string names of criteria to numpy arrays with
their values for each example
(Different AttackGoals track different criteria)
:param run_counts: dict mapping AttackConfigs to numpy arrays reporting how
many times they have been run on each example
:param goal: the AttackGoal to work on
:param report: dict, see `bundle_attacks_with_goal`
:param report_path: str, path to save the report to
"""
attack_config = goal.get_attack_config(attack_configs, run_counts,
criteria)
idxs = goal.request_examples(attack_config, criteria, run_counts,
BATCH_SIZE)
x_batch = x[idxs]
assert x_batch.shape[0] == BATCH_SIZE
y_batch = y[idxs]
assert y_batch.shape[0] == BATCH_SIZE
adv_x_batch = run_attack(sess, model, x_batch, y_batch,
attack_config.attack, attack_config.params,
BATCH_SIZE, devices)
criteria_batch = goal.get_criteria(sess, model, adv_x_batch, y_batch)
# This can't be parallelized because some orig examples are copied more
# than once into the batch
cur_run_counts = run_counts[attack_config]
for batch_idx, orig_idx in enumerate(idxs):
cur_run_counts[orig_idx] += 1
should_copy = goal.new_wins(criteria, orig_idx, criteria_batch,
batch_idx)
if should_copy:
adv_x_val[orig_idx] = adv_x_batch[batch_idx]
for key in criteria:
criteria[key][orig_idx] = criteria_batch[key][batch_idx]
assert np.allclose(y[orig_idx], y_batch[batch_idx])
report['bundled'] = {
'correctness': criteria['correctness'],
'confidence': criteria['confidence']
}
should_save = False
new_time = time.time()
if 'time' in report:
if new_time - report['time'] > REPORT_TIME_INTERVAL:
should_save = True
else:
should_save = True
if should_save:
report['time'] = new_time
goal.print_progress(criteria, run_counts)
print_stats(criteria['correctness'], criteria['confidence'], 'bundled')
serial.save(report_path, report)
assert report_path.endswith(".joblib")
adv_x_path = report_path[:-len(".joblib")] + "_adv.npy"
np.save(adv_x_path, adv_x_val)