xs_ph = tf.placeholder(model.x_dtype, shape=(batch_size, *model.x_shape))
lgs, lbs = model.logits_and_labels(xs_ph)
def iteration_callback(xs, xs_adv):
delta = tf.abs(xs_adv - xs)
return tf.reduce_max(tf.reshape(delta, (xs.shape[0], -1)), axis=1)
loss = CrossEntropyLoss(model)
attack = BIM(
model=model,
batch_size=batch_size,
loss=loss,
goal='ut',
distance_metric='l_inf',
session=session,
iteration_callback=iteration_callback,
)
attack.config(
iteration=10,
magnitude=8.0 / 255.0,
alpha=1.0 / 255.0,
)
for lo in range(0, batch_size, batch_size):
xs = xs_test[lo:lo + batch_size]
ys = ys_test[lo:lo + batch_size]
try:
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../example/imagenet/resnet152_fd.py')
rs_model = load_model_from_path(model_path)
model = rs_model.load(session)
xs_ph = tf.placeholder(model.x_dtype, shape=(batch_size, *model.x_shape))
lgs, lbs = model.logits_and_labels(xs_ph)
dataset = imagenet.load_dataset_for_classifier(model, load_target=True)
dataset = dataset.batch(batch_size).take(10)
loss = CrossEntropyLoss(model)
attack = BIM(model=model,
batch_size=batch_size,
loss=loss,
goal='ut',
distance_metric='l_inf',
session=session)
attack.config(
iteration=50,
magnitude=8.0 / 255.0,
alpha=0.5 / 255.0,
)
accs, adv_accs = [], []
for filenames, xs, ys, ys_target in dataset_to_iterator(dataset, session):
xs_adv = attack.batch_attack(xs, ys=ys)
lbs_pred = session.run(lbs, feed_dict={xs_ph: xs})
lbs_adv = session.run(lbs, feed_dict={xs_ph: xs_adv})