def adv_acc(y, _):
# Generate adversarial examples
x_adv = fgsm(model, y, eps=eps, clip_min=clip_min, clip_max=clip_max)
# Consider the attack to be constant
x_adv = K.stop_gradient(x_adv)
# Accuracy on the adversarial examples
preds_age, preds_race, preds_gender = model(x_adv)
return categorical_accuracy(y, preds_race)
def adv_acc(y, _):
# Generate adversarial examples
y_gender = tf.get_default_graph().get_tensor_by_name("gender_target:0")
x_adv = fgsm(model, y_gender, eps=eps, clip_min=clip_min, clip_max=clip_max)
# Consider the attack to be constant
x_adv = K.stop_gradient(x_adv)
# Accuracy on the adversarial examples
_, preds_age = model(x_adv)
return categorical_accuracy(y, preds_age)
def adv_acc(y, _):
# Generate adversarial examples
#y_race = tf.get_default_graph().get_tensor_by_name("race_target:0")
x_adv = fgsm(model, y, eps=eps, clip_min=clip_min, clip_max=clip_max)
# Consider the attack to be constant
x_adv = K.stop_gradient(x_adv)
# Accuracy on the adversarial examples
preds_age, preds_race, preds_gender = model(x_adv)
return mae(y, preds_age)
