def recipe(
sess,
model,
x,
y,
nb_classes,
eps,
clip_min,
clip_max,
eps_iter,
nb_iter,
report_path,
eps_iter_small,
batch_size,
):
"""
Mock recipe that just runs the Noise attack so the test runs fast
"""
attack_configs = [AttackConfig(Noise(model, sess), {"eps": eps})]
new_work_goal = {config: 1 for config in attack_configs}
goals = [Misclassify(new_work_goal=new_work_goal)]
bundle_attacks(
sess,
model,
x,
y,
attack_configs,
goals,
report_path,
attack_batch_size=batch_size,
eval_batch_size=batch_size,
)
def test_misclassify_request_examples():
cfg = AttackConfig(None, None)
goal = Misclassify(new_work_goal={cfg: 1})
correctness = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1], dtype=np.bool)
run_counts = np.array([1, 1, 1, 0, 0, 0, 1, 1, 1, 0], dtype=np.int64)
criteria = {'correctness': correctness}
batch_size = 3
idxs = goal.request_examples(cfg, criteria, {cfg: run_counts}, batch_size)
assert idxs.shape == (batch_size,)
idxs = list(idxs)
for already_misclassified in [0, 2, 4, 6, 8]:
assert already_misclassified not in idxs
for already_run in [1, 7]:
assert already_misclassified not in idxs
for needed in [3, 5, 9]:
assert needed in idxs
def recipe(sess, model, x, y, nb_classes, eps, clip_min,
clip_max, eps_iter, nb_iter,
report_path, eps_iter_small):
attack_configs = [AttackConfig(Noise(model, sess), {'eps': eps})]
new_work_goal = {config: 1 for config in attack_configs}
goals = [Misclassify(new_work_goal=new_work_goal)]
bundle_attacks(sess, model, x, y, attack_configs, goals, report_path, attack_batch_size=batch_size,
eval_batch_size=batch_size)