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attack.py
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attack.py
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import matplotlib
matplotlib.use("Agg")
import argparse
import gc
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
import seaborn as sns
import scipy
import sys
import tensorflow as tf
import time
from draw import DRAW
from cvae import ConvVAE
from vae import VAE
import celeba_utils
import svhn_utils
import mnist_utils
def print_log(s='', verbose=True):
LOG_FP.write(s + '\n')
if verbose:
print(s)
def optimize_noise(vae, orig_img, target_img, C, attack_output=False,
bfgs=True):
vae_x = orig_img.reshape(-1, data_utils.img_dim,
data_utils.img_dim, data_utils.n_chan)
if not attack_output:
adv_mean, adv_log_var = vae.batch_transform(
data_utils.input(target_img, batch_size))
adv_mean = adv_mean[0]
adv_log_var = adv_log_var[0]
adv_target_output = None
else:
adv_mean = None
adv_log_var = None
adv_target_output = target_img
if bfgs:
x, adv_loss = optimize_noise_bfgs(vae, orig_img, vae_x, adv_mean,
adv_log_var, adv_target_output, C)
else:
x, adv_loss = vae.batch_optimize_attack(
data_utils.input(vae_x, batch_size),
adv_mean,
adv_log_var,
C,
adv_target_output,
num_iter=NUM_ITER
)
return x, adv_loss
def optimize_noise_bfgs(vae, orig_img, vae_x, adv_mean, adv_log_var,
adv_target_output, C):
init_noise = np.random.uniform(
-1e-8,
1e-8,
size=(data_utils.img_dim, data_utils.img_dim, data_utils.n_chan)
).astype(np.float32)
adv_loss = []
def fmin_func(noise):
vae.set_noise(
noise.reshape(
data_utils.img_dim, data_utils.img_dim, data_utils.n_chan
)
)
loss, grad = vae.batch_evaluate_attack(
data_utils.input(vae_x, batch_size),
adv_mean,
adv_log_var,
C,
adv_target_output
)
adv_loss.append(loss)
return float(loss), grad.flatten().astype(np.float64)
# Noise bounds (pixels cannot exceed 0-1)
# ToDo: correct limits
bounds = zip(
-data_utils.data_mean / data_utils.data_std - orig_img.flatten(),
(data_utils.max_pixel - data_utils.data_mean) / data_utils.data_std -
orig_img.flatten()
)
bounds = [sorted(x) for x in bounds]
# L-BFGS-B optimization to find adversarial noise
x, f, d = scipy.optimize.fmin_l_bfgs_b(
fmin_func,
x0=init_noise,
bounds=bounds,
m=25,
factr=10
)
x = x.reshape(data_utils.img_dim, data_utils.img_dim, data_utils.n_chan)
return x, adv_loss
def adv_test(vae, orig_img, target_img, C, plot=True):
vae.reset_noise()
# Original and target reconstruction
original_reconstructions = vae.batch_reconstruct(
data_utils.input(orig_img, batch_size))
target_reconstructions = vae.batch_reconstruct(
data_utils.input(target_img, batch_size))
orig_recon_dist, orig_recon_dist_std = data_utils.dist(
original_reconstructions, orig_img, batch_size)
target_recon_dist, target_recon_dist_std = data_utils.dist(
target_reconstructions, target_img, batch_size)
orig_target_recon_dist, orig_target_recon_dist_std = data_utils.dist(
original_reconstructions, target_img, batch_size)
target_orig_recon_dist, target_orig_recon_dist_std = data_utils.dist(
target_reconstructions, orig_img, batch_size)
x, adv_loss = \
optimize_noise(vae, orig_img, target_img, C, ATTACK_OUTPUT, BFGS)
adv_input = x + orig_img
# Adversarial reconstruction
vae.reset_noise()
adv_imgs = vae.batch_reconstruct(data_utils.input(adv_input, batch_size))
orig_dist, orig_dist_std = data_utils.dist(
adv_imgs, orig_img, batch_size)
adv_dist, adv_dist_std = data_utils.dist(
adv_imgs, target_img, batch_size)
recon_dist, recon_dist_std = data_utils.dist(
adv_imgs, adv_input, batch_size)
# Plotting results
if plot:
data_utils.show(orig_img, True, 1, "Original")
data_utils.show(
original_reconstructions[0], True, 2, "Original rec.")
data_utils.show(adv_input, True, 3, "Adversarial")
data_utils.show(target_img, True, 4, "Target")
data_utils.show(adv_imgs[0], True, 5, "Adversarial rec.")
data_utils.show(x, True, 6, "Distortion")
plt.savefig(model_dir + ('/results/exp_%d_imgs.png' % FIG_COUNT))
plt.close()
plt.figure()
plt.scatter(range(len(adv_loss)), adv_loss)
plt.ylabel('adv_loss')
plt.xlabel('iter')
plt.savefig(model_dir + ('/results/exp_%d_loss.png' % FIG_COUNT))
plt.close()
np.save(model_dir + ('/results/exp_%d_noise' % FIG_COUNT), x)
orig_target_dist = np.linalg.norm(orig_img - target_img)
returns = (
np.linalg.norm(x),
orig_dist,
orig_dist_std,
adv_dist,
adv_dist_std,
orig_recon_dist,
orig_recon_dist_std,
target_recon_dist,
target_recon_dist_std,
recon_dist,
recon_dist_std,
orig_target_dist,
orig_target_recon_dist,
orig_target_recon_dist_std,
target_orig_recon_dist,
target_orig_recon_dist_std,
adv_loss[-1]
)
return returns
def orig_adv_dist(vae, orig_img=None, target_img=None, plot=False, bestC=None):
if orig_img is None:
orig_img = np.random.randint(0, len(test_x))
if target_img is None:
target_img = orig_img
while np.array_equal(target_img, orig_img):
target_img = np.random.randint(0, len(test_x))
noise_dist = []
orig_dist = []
orig_dist_std = []
adv_dist = []
adv_dist_std = []
target_recon_dist = []
target_recon_dist_std = []
recon_dist = []
recon_dist_std = []
orig_target_dist = []
orig_target_recon_dist = []
orig_target_recon_dist_std = []
target_orig_recon_dist = []
target_orig_recon_dist_std = []
adv_loss = []
C = np.logspace(-20, 20, NUM_POINTS, base=2, dtype=np.float32)
C = np.concatenate(([0], C))
for c in C:
noise, od, ods, ad, ads, ore, ores, tre, tres, recd, recs, otd, otrd, \
otrds, tord, tords, advl = adv_test(
vae, test_x[orig_img], test_x[target_img], C=c, plot=False)
noise_dist.append(noise)
orig_dist.append(od)
orig_dist_std.append(ods)
adv_dist.append(ad)
adv_dist_std.append(ads)
target_recon_dist.append(tre)
target_recon_dist_std.append(tres)
recon_dist.append(recd)
recon_dist_std.append(recs)
orig_target_dist.append(otd)
orig_target_recon_dist.append(otrd)
orig_target_recon_dist_std.append(otrds)
target_orig_recon_dist.append(tord)
target_orig_recon_dist_std.append(tords)
adv_loss.append(advl)
noise_dist = np.array(noise_dist)
orig_dist = np.array(orig_dist)
orig_dist_std = np.array(orig_dist_std)
adv_dist = np.array(adv_dist)
adv_dist_std = np.array(adv_dist_std)
target_recon_dist = np.array(target_recon_dist)
target_recon_dist_std = np.array(target_recon_dist_std)
recon_dist = np.array(recon_dist)
recon_dist_std = np.array(recon_dist_std)
orig_target_dist = np.array(orig_target_dist)
orig_target_recon_dist = np.array(orig_target_recon_dist)
orig_target_recon_dist_std = np.array(orig_target_recon_dist_std)
target_orig_recon_dist = np.array(target_orig_recon_dist)
target_orig_recon_dist_std = np.array(target_orig_recon_dist_std)
adv_loss = np.array(adv_loss)
if bestC is None:
tmp_idx = adv_dist <= np.mean(adv_dist)
bestC = np.atleast_1d(
np.atleast_1d(C[tmp_idx])[np.argmax(adv_dist[tmp_idx])]
)[0]
ex_noise, _, _, ex_adv_dist, _, orig_reconstruction_dist, _, \
target_reconstruction_dist, _, _, _, ex_orig_target_dist, \
ex_orig_target_recon_dist, _, _, _, _ = adv_test(
vae, test_x[orig_img], test_x[target_img], C=bestC, plot=plot)
print_log(
"orig_img=%d, target_img=%d, bestC=%f, adv_dist=%f, noise_norm=%f"
% (orig_img, target_img, bestC, ex_adv_dist, np.linalg.norm(ex_noise))
)
if plot:
plt.figure()
plt.axvline(x=ex_orig_target_dist, linewidth=2,
color='cyan', label="Original - Target")
plt.axhline(y=ex_orig_target_recon_dist, linewidth=2,
color='DarkOrange', label="Original rec. - Target")
plt.axhline(y=target_reconstruction_dist, linewidth=2,
color='red', label="Target rec. - Target")
plt.scatter(noise_dist, adv_dist)
plt.scatter([ex_noise], [ex_adv_dist], color="red")
plt.ylabel("Adversarial rec. - Target")
plt.xlabel("Distortion")
plt.legend()
plt.savefig(model_dir + ('/results/exp_%d.png' % FIG_COUNT))
plt.close()
# Adversarial Loss
plt.figure()
plt.scatter(noise_dist, adv_loss)
plt.ylabel("Adversarial Loss")
plt.xlabel("Distortion")
plt.savefig(model_dir + ('/results/exp_%d_adv_loss.png' % FIG_COUNT))
plt.close()
df = pd.DataFrame(
{
'orig_img': orig_img,
'target_img': target_img,
'bestC': bestC,
'orig_reconstruction_dist': orig_reconstruction_dist,
'target_reconstruction_dist': target_reconstruction_dist,
'noise_dist': noise_dist,
'orig_dist': orig_dist,
'orig_dist_std': orig_dist_std,
'adv_dist': adv_dist,
'adv_dist_std': adv_dist_std,
'target_recon_dist': target_recon_dist,
'target_recon_dist_std': target_recon_dist_std,
'recon_dist': recon_dist,
'recon_dist_std': recon_dist_std,
'orig_target_dist': orig_target_dist,
'orig_target_recon_dist': orig_target_recon_dist,
'orig_target_recon_dist_std': orig_target_recon_dist_std,
'target_orig_recon_dist': target_orig_recon_dist,
'target_orig_recon_dist_std': target_orig_recon_dist_std,
'C': C
}
)
with open(model_dir + ("/results/exp_%d.csv" % FIG_COUNT), 'w') as fp:
df.to_csv(fp)
def run_experiments(pairs):
global FIG_COUNT
n = len(pairs)
for i in range(STARTING_FIG, n):
FIG_COUNT = i
start_time = time.time()
print_log("----------------------------------------")
print_log("Experiment %d/%d" % (i + 1, n))
orig_adv_dist(
vae,
orig_img=pairs[i][0],
target_img=pairs[i][1],
plot=True
)
print_log("\tTime %f sec" % (time.time() - start_time))
print_log()
gc.collect()
if __name__ == "__main__":
np.random.seed(0)
tf.set_random_seed(0)
sns.set()
parser = argparse.ArgumentParser()
parser.add_argument('--dir', type=str, default='./celeba/draw')
parser.add_argument('--num_attacks', type=int, default=20)
parser.add_argument('--num_iter', type=int, default=3000)
parser.add_argument('--num_points', type=int, default=50)
parser.add_argument('--starting_fig', type=int, default=0)
parser.add_argument('--attack_output', dest='attack_output',
action='store_true')
parser.set_defaults(attack_output=False)
parser.add_argument('--bfgs', dest='bfgs', action='store_true')
parser.set_defaults(bfgs=False)
# Parser
args, unknown = parser.parse_known_args()
if len(unknown) > 0:
print("Invalid arguments %s" % unknown)
parser.print_log_help()
sys.exit()
args = vars(parser.parse_args())
model_dir = args['dir']
# Networks architecture
with open(model_dir + '/architecture.pkl', 'rb') as fp:
config = pickle.load(fp)
LOG_FP = open(model_dir + '/attacker.log', 'a+')
if config['dataset'] == 'celeba':
data_utils = celeba_utils
train_x, val_x, test_x = data_utils.load_data(config['is_gaussian'])
print_log("CelebA loaded")
elif config['dataset'] == 'svhn':
data_utils = svhn_utils
train_x, _, val_x, _, test_x, _ = data_utils.load_data(
config['is_gaussian'])
print_log("SVHN loaded")
elif config['dataset'] == 'mnist':
data_utils = mnist_utils
train_x, _, val_x, _, test_x, _ = data_utils.load_data(
config['is_gaussian'])
print_log("MNIST loaded")
else:
sys.exit("No dataset %s" % args['dataset'])
print_log("Training samples %d" % train_x.shape[0])
print_log("Validation samples %d" % val_x.shape[0])
print_log("Test samples %d" % test_x.shape[0])
print_log("----------------------------------------")
pairs = []
for i in range(args['num_attacks']):
orig_img = np.random.randint(0, len(test_x))
target_img = orig_img
while np.array_equal(target_img, orig_img):
target_img = np.random.randint(0, len(test_x))
pairs.append([orig_img, target_img])
# Load model
tf.reset_default_graph()
config['is_attacking'] = True
if config['architecture'] == 'draw':
vae = DRAW(config)
print_log("----------------------------------------")
print_log("DRAW graph loaded")
elif config['architecture'] == 'cvae':
vae = ConvVAE(config)
print_log("----------------------------------------")
print_log("ConvVAE graph loaded")
elif config['architecture'] == 'vae':
vae = VAE(config)
print_log("----------------------------------------")
print_log("VAE graph loaded")
else:
sys.exit("No architecture %s" % config['architecture'])
tf.get_default_graph().finalize()
vae.load(tf.train.latest_checkpoint(model_dir + '/model/'))
batch_size = vae.batch_size
ATTACK_OUTPUT = args['attack_output']
BFGS = args['bfgs']
NUM_ITER = args['num_iter']
FIG_COUNT = None
STARTING_FIG = args['starting_fig']
NUM_POINTS = args['num_points']
run_experiments(pairs)
vae.close()