5e1845ab9b7e0fa54affcaf6d1183dcdc1bc3d58,tutorials/mnist_blackbox.py,,main,#Any#,188

Before Change

    substitute_preds = train_substitute(sess, x, y, bbox_preds, X_sub, Y_sub)

    // Craft adversarial examples using the substitute
    adv_x = fgsm(x, substitute_preds, eps=0.3)
    eval_params = {"batch_size": FLAGS.batch_size}
    X_test_adv, = batch_eval(sess, [x], [adv_x], [X_test], args=eval_params)

    // Evaluate the accuracy of the "black-box" model on adversarial examples
    accuracy = model_eval(sess, x, y, bbox_preds, X_test_adv, Y_test,
                          args=eval_params)

After Change

    substitute_preds = train_substitute(sess, x, y, bbox_preds, X_sub, Y_sub)

    // Initialize the Fast Gradient Sign Method (FGSM) attack object.
    FGSM = FastGradientMethod(x, substitute_preds, sess=sess, clip_min=0.,
                              clip_max=1., params={"eps": 0.3, "ord": np.inf})

    // Craft adversarial examples using the substitute
    eval_params = {"batch_size": FLAGS.batch_size}
    X_test_adv = FGSM.craft(X_test)

    // Evaluate the accuracy of the "black-box" model on adversarial examples
    accuracy = model_eval(sess, x, y, bbox_preds, X_test_adv, Y_test,
                          args=eval_params)

Instances