generated_img_adv = tf.reverse(generated_img_adv,
axis=[-1]) # switch RGB to BGR
adv_img_bgr = generated_img_adv
generated_img_adv = stn.encoder.preprocess(
generated_img_adv) # preprocess image
enc_gen_adv, enc_gen_layers_adv = stn.encoder.encode(generated_img_adv)
generated_img = tf.reverse(generated_img, axis=[-1]) # switch RGB to BGR
img_bgr = generated_img
generated_img = stn.encoder.preprocess(generated_img) # preprocess image
enc_gen, enc_gen_layers = stn.encoder.encode(generated_img)
if data_set == "cifar10":
if model_name in ["cifar10_nat", "cifar10_adv"]:
classifier = Model("eval", raw_cifar.train_images)
classifier._build_model(adv_img, label, reuse=False, conf=0.1)
adv_loss = -classifier.target_loss
adv_acc = classifier.accuracy
adv_acc_y = tf.cast(classifier.correct_prediction, tf.float32)
classifier._build_model(content, label, reuse=True, conf=0.1)
normal_loss = -classifier.target_loss
norm_acc = classifier.accuracy
acc_y = tf.cast(classifier.correct_prediction, tf.float32)
classifier._build_model(img, label, reuse=True)
decode_acc_y = tf.cast(classifier.correct_prediction, tf.float32)
elif model_name in ["cifar10_trades"]:
classifier = build_model_trades(adv_img, label, conf=0.1)
#classifier._build_model(adv_img, label, reuse=False, conf=0.1)
adv_loss = -classifier.target_loss
adv_acc = classifier.accuracy
adv_acc_y = tf.cast(classifier.correct_prediction, tf.float32)
# pass the generated_img to the encoder, and use the output compute loss
generated_img_adv = tf.reverse(generated_img_adv,
axis=[-1]) # switch RGB to BGR
adv_img_bgr = generated_img_adv
generated_img_adv = stn.encoder.preprocess(
generated_img_adv) # preprocess image
enc_gen_adv, enc_gen_layers_adv = stn.encoder.encode(generated_img_adv)
generated_img = tf.reverse(generated_img, axis=[-1]) # switch RGB to BGR
img_bgr = generated_img
generated_img = stn.encoder.preprocess(generated_img) # preprocess image
enc_gen, enc_gen_layers = stn.encoder.encode(generated_img)
if data_set == "cifar10":
classifier = Model("eval", raw_cifar.train_images)
classifier._build_model(adv_img, label, reuse=False)
adv_loss = -classifier.relaxed_y_xent
adv_acc = classifier.accuracy
classifier._build_model(img, label, reuse=True)
normal_loss = -classifier.relaxed_y_xent
norm_acc = classifier.accuracy
elif data_set == "imagenet":
classifier = build_imagenet_model(adv_img_bgr, label)
losses = tf.stack([
classifier.rev_xent, classifier.poss_loss, classifier.xent,
classifier.xent_filter
])
adv_loss = -tf.reduce_sum(losses[loss_choice])
adv_acc = classifier.accuracy
acc_y = classifier.acc_y
classifier = build_imagenet_model(img_bgr, label, reuse=True)
_d["stn_vars"] = get_scope_var(aname1 + "/" + "transform")
# get the target feature maps which is the output of AdaI
# pass the generated_img to the encoder, and use the output compute loss
_d["generated_img_adv"] = tf.reverse(
_d["generated_img_adv"], axis=[-1]) # switch RGB to BGR
adv_img_bgr = _d["generated_img_adv"]
_d["generated_img_adv"] = stn.encoder.preprocess(
_d["generated_img_adv"]) # preprocess image
_d["enc_gen_adv"], _d[
"enc_gen_layers_adv"] = stn.encoder.encode(
_d["generated_img_adv"])
if data_set == "cifar10":
classifier._build_model(_d["adv_img"],
label,
reuse=not first_use,
conf=5)
_d["adv_acc_y"] = tf.cast(classifier.correct_prediction,
tf.float32)
else:
classifier = build_imagenet_model(adv_img_bgr,
label,
conf=5,
reuse=not first_use)
_d["adv_acc_y"] = classifier.acc_y
first_use = False
_d["adv_loss"] = -classifier.target_loss
_d["adv_acc"] = classifier.accuracy
if data_set == "cifar10":
classifier._build_model(content, label, reuse=True, conf=5)
