nn_robust_attacks
nn_robust_attacks copied to clipboard
carlini
Robust evasion attacks against neural network to find adversarial examples
I am getting the following error on running the train.py file: ``` File "train_models.py", line 110, in train(MNIST(), "models/mnist", [32, 32, 64, 64, 200, 200], num_epochs=50) File "/users/btech/ananyag/Desktop/ugp/nn_robust_attacks/setup_mnist.py", line 49,...
hello, after reading your paper, I have a question, what's the meaning of this sentence: `self.newimg = tf.tanh(modifier + self.timg) * self.boxmul + self.boxplus` the input data is between [-0.5,0.5],...
in l2 completion,you use codes like below. What is the meaning?And why you use 10000?Could you tell me some more detail? other = tf.reduce_max((1-self.tlab)*self.output - (self.tlab*10000),1)
I ran the script to train the model on CIFAR10 and also the L0 attack on the trained model. However, the validation accuracy achieved by the script is very low....
```During handling of the above exception, another exception occurred: Traceback (most recent call last): File "test_attack.py", line 69, in data, model = MNIST(), MNISTModel("models/mnist", sess) File "/Users/datle/Documents/Secure_machine_learning/SVM/Carlini/nn_robust_attacks/setup_mnist.py", line 89, in...
Traceback (most recent call last): File "/content/drive/MyDrive/Codes/carlini_nn_robust_attacks/nn_robust_attacks/train_models.py", line 113, in num_epochs=1, train_temp=100) File "/content/drive/MyDrive/Codes/carlini_nn_robust_attacks/nn_robust_attacks/train_models.py", line 88, in train_distillation init=file_name+"_init") File "/content/drive/MyDrive/Codes/carlini_nn_robust_attacks/nn_robust_attacks/train_models.py", line 50, in train model.load_weights(init) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py", line 2227,...
In li_attack.py, the function `doit` never returns anything if `abort_early=False`. This means that `doit` will always return None, which will be interpreted by `attack_single` as a failure.
I am using l0_attack.py. The default printing shows: ``` equal_count = self.model.image_size**2-np.sum(np.all(np.abs(img-nimg[0])
