model = Vgg16()
k=6
#learning_rate = 1e-4
#optimizer = torch.optim.Adam(x_adv, lr=learning_rate)
loss = None
x_adv = image
x_adv.retain_grad()
for i in range(1000):
preds = model(x_adv)
internal_logits = preds[-2]
final_logits = preds[-1]
label = np.argmax(variable_to_numpy(final_logits))
loss = internal_logits.std()
# save image
x_adv_np = x_adv.cpu().detach().numpy()
x_adv_np = np.squeeze(x_adv_np)
x_adv_np = np.transpose(x_adv_np, (1, 2, 0))
x_adv_np = (x_adv_np * 255).astype(np.uint8)
if (i % 50 == 0):
Image.fromarray(x_adv_np).save('./out/car_%d.jpg' % i)
google_label = detect_label('./out/car_%d.jpg' % i)
print(i, variable_to_numpy(loss), imagenet_dict[label])
print(google_label)
loss.backward(retain_graph=True)
for layer_idx, (intermediate_logit_var,
intermediate_logit_adv_var) in enumerate(
tqdm(zip(internal_logits_var, internal_logits_adv_var))):
intermediate_features = intermediate_logit_var[0].detach().cpu().numpy()
intermediate_adv_features = intermediate_logit_adv_var[0].detach().cpu(
).numpy()
visualize_features_compare(
intermediate_features,
intermediate_adv_features,
output_dir='results',
file_prefix='compare_{0:02d}_'.format(layer_idx),
data_format='channels_first',
only_first_channel=True)
adv_np = variable_to_numpy(adv)
image_pil = Image.fromarray(
np.transpose((adv_np * 255).astype(np.uint8), (1, 2, 0)))
image_pil.save(os.path.join("results", 'adv_at_{0:02d}.png'.format(10)))
linf = int(np.max(abs(image_np - adv_np)) * 255)
print('linf: ', linf)
l1 = np.mean(abs(image_np - adv_np)) * 255
print('l1: ', l1)
l2 = np.sqrt(
np.mean(
np.multiply((image_np * 255 - adv_np * 255),
(image_np * 255 - adv_np * 255))))
print('l2: ', l2)
print(" ")