I am trying to follow this blog https://brunolopezgarcia.github.io/2018/05/09/Crafting-adversarial-faces.html to generate adversarial face images against Facenet. The code is here https://github.com/tensorflow/cleverhans/tree/master/examples/facenet_adversarial_faces and works fine! My question is how can I export these adversarial images. Is this question too straightforward, so the blog didn't mention it, but only shows some sample pictures.
I was thinking it is not a hard problem, since I know the generated adversarial samples are in the "adv". But this adv (float32) came from faces1, after being prewhiten and normalized. To restore the int8 images from adv(float32), I have to reverse the normalization and prewhiten process. It seems like if we want output some images from facenet, we have to do this process.
I am new to Facenet and Cleverhans, I am not sure whether this is the best way to do that, or is that common way(such as functions) for people to export images from Facenet.
In facenet_fgsm.py, we finally got the adversarial samples. I need to export adv to plain int images.
adv = sess.run(adv_x, feed_dict=feed_dict)
In set_loader.py. There are some kinda of normalization.
def load_testset(size):
# Load images paths and labels
pairs = lfw.read_pairs(pairs_path)
paths, labels = lfw.get_paths(testset_path, pairs, file_extension)
# Random choice
permutation = np.random.choice(len(labels), size, replace=False)
paths_batch_1 = []
paths_batch_2 = []
for index in permutation:
paths_batch_1.append(paths[index * 2])
paths_batch_2.append(paths[index * 2 + 1])
labels = np.asarray(labels)[permutation]
paths_batch_1 = np.asarray(paths_batch_1)
paths_batch_2 = np.asarray(paths_batch_2)
# Load images
faces1 = facenet.load_data(paths_batch_1, False, False, image_size)
faces2 = facenet.load_data(paths_batch_2, False, False, image_size)
# Change pixel values to 0 to 1 values
min_pixel = min(np.min(faces1), np.min(faces2))
max_pixel = max(np.max(faces1), np.max(faces2))
faces1 = (faces1 - min_pixel) / (max_pixel - min_pixel)
faces2 = (faces2 - min_pixel) / (max_pixel - min_pixel)
In the facenet.py load_data function, there is a prewhiten process.
nrof_samples = len(image_paths)
images = np.zeros((nrof_samples, image_size, image_size, 3))
for i in range(nrof_samples):
img = misc.imread(image_paths[i])
if img.ndim == 2:
img = to_rgb(img)
if do_prewhiten:
img = prewhiten(img)
img = crop(img, do_random_crop, image_size)
img = flip(img, do_random_flip)
images[i,:,:,:] = img
return images
I hope some expert can point me some hidden function in facenet or cleverhans that can directly export the adv images, otherwise reversing normalization and prewhiten process seems akward. Thank you very much.
