/
gen_embs.py
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/
gen_embs.py
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import argparse
import os
import random
import shutil
import sys
import keras.models
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn.decomposition import PCA
from sklearn.preprocessing import normalize
import gen_utils
from inject_utils import eval_attack
from task_utils import load_attack
BASE_DIR = 'results'
def main():
cur_results_dir = os.path.join(BASE_DIR, "{}".format(args.config))
gen_utils.init_gpu(args.gpu)
dataset, task, injected_X, injected_Y, X_test, Y_test, injected_X_test, injected_Y_test, is_backdoor_ls, number_train, feature_layer_name, num_classes, target_label = load_attack(
args.config, load_clean=None)
task.injected_X = injected_X
task.injected_Y = injected_Y
task.injected_X_test = injected_X_test
task.injected_Y_test = injected_Y_test
task.is_backdoor_ls = is_backdoor_ls
task.target_label = target_label
if dataset == 'physical' or dataset == 'imagenet':
BATCH_SIZE = 30
else:
BATCH_SIZE = 256
model = keras.models.load_model(f"models/{dataset}_{args.config}_model.h5")
succ_attack_idx = model.predict(injected_X_test)
succ_attack_idx = np.argmax(succ_attack_idx, axis=1) == np.argmax(injected_Y_test, axis=1)
injected_X_test = injected_X_test[succ_attack_idx]
injected_Y_test = injected_Y_test[succ_attack_idx]
attack_accuracy = eval_attack(model, injected_X_test, injected_Y_test)[1]
assert attack_accuracy > 0.6
classification_accuracy = eval_attack(model, X_test, Y_test)[1]
print("classification_accuracy: {:.2f}".format(classification_accuracy))
task.model = model
# model.summary()
for l in model.layers:
l.trainable = True
layer_of_interest = None
for idx, variable in enumerate(model.trainable_variables[::-1][:10]):
if "kernel" in variable.name.lower():
layer_of_interest = variable
break
if (layer_of_interest is None) or len(layer_of_interest.shape) != 2:
raise Exception("Selected Layer is problematic. Please check you have a dense layer at the end of the model. ")
print("Selected Layer: ", layer_of_interest.name)
full_size_of_embedding = layer_of_interest.shape[0] * layer_of_interest.shape[1]
print("Full embedding size: {}".format(full_size_of_embedding))
size_kept = int(full_size_of_embedding * args.ratio)
if size_kept > 5000:
size_kept = 5000
kept_mask = random.sample(list(range(full_size_of_embedding)), size_kept)
gradient_list = np.zeros((number_train, size_kept))
print("Shape: ", gradient_list.shape)
for i, batch_i in enumerate(range(0, number_train, BATCH_SIZE)):
print("Batch: ", i)
inputs = injected_X[batch_i:batch_i + BATCH_SIZE]
labels = injected_Y[batch_i:batch_i + BATCH_SIZE]
with tf.GradientTape() as tape:
ypred = model(inputs, training=False)
labels = tf.ones(labels.shape) * 1 / num_classes
loss = tf.keras.losses.categorical_crossentropy(labels, ypred)
jacobian = tape.jacobian(loss, layer_of_interest)
jacobian = np.array(jacobian)
jacobian = np.reshape(jacobian, (len(inputs), -1))
reduced_jacobian = jacobian[:, kept_mask]
gradient_list[batch_i:batch_i + BATCH_SIZE] = reduced_jacobian
# np.sum(np.abs(jacobian), axis=1)
# import pdb
# pdb.set_trace()
gradients = np.array(gradient_list.reshape(number_train, -1))
embedding = gradients
print("Embedding Shape: {}, pid: {}".format(embedding.shape, os.getpid()))
if os.path.exists(cur_results_dir):
shutil.rmtree(cur_results_dir)
normalized_embedding = normalize(embedding)
os.mkdir(cur_results_dir)
np.save(os.path.join(cur_results_dir, "embedding_norm.p"), normalized_embedding)
np.save(os.path.join(cur_results_dir, "embedding.p"), embedding)
if args.pca:
plot_pca(embedding, is_backdoor_ls, injected_Y, task.target_label, os.path.join(cur_results_dir, "pca.png"))
plot_pca(normalized_embedding, is_backdoor_ls, injected_Y, task.target_label,
os.path.join(cur_results_dir, "pca_norm.png"))
def plot_pca(cur_embs, is_backdoor_ls, injected_Y, target_y, output_file):
fig = plt.figure()
pca = PCA(n_components=2).fit(cur_embs)
pca_results = pca.fit_transform(cur_embs)
backdoor_pca = np.array([p for i, p in enumerate(pca_results) if is_backdoor_ls[i]])
clean_pca = np.array([p for i, p in enumerate(pca_results) if not is_backdoor_ls[i]])
target_clean_pca = np.array(
[p for i, p in enumerate(pca_results) if injected_Y[i][target_y] == 1 and not is_backdoor_ls[i]])
plt.scatter(clean_pca[:, 0], clean_pca[:, 1], label="Clean X", marker=".", color="red", alpha=0.05)
plt.scatter(target_clean_pca[:, 0], target_clean_pca[:, 1], label="Target clean", marker=".", color='g', alpha=0.05)
plt.scatter(backdoor_pca[:, 0], backdoor_pca[:, 1], label="Backdoor X", marker=".", color='b', alpha=0.05)
plt.legend()
plt.savefig(output_file)
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', '-g', type=str,
help='GPU id', default='0')
parser.add_argument('--config', '-c', type=str,
help='name of dataset', default='cifar1')
parser.add_argument('--pca', action='store_true')
parser.add_argument('--ratio', '-r', type=float,
help='name of dataset', default=1.0)
return parser.parse_args(argv)
if __name__ == '__main__':
args = parse_arguments(sys.argv[1:])
main()