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Code and experiments for the adversarial detection paper

License: MIT License

Python 100.00%

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adversarial-detection's Issues

Details on parameters of adversarial attack?

Greetings!
Thank you for sharing the codes and it is really a solid work! But I got several questions about the parameters of adversarial attack.

As put in Section D.4 in supplementary file, the parameters of PGD attack are epsilon=1/255, stepsize=0.05, iteration=40. However, the epsilon is smaller than stepsize (1/255≈0.0039<0.05). Is there a mistake?
I wonder what does the epsilon linearly spaced in the interval [1/255, 21/255] means. As put in Section D.4 in supplementary file, the epsilon of PGD attack is set as 1/255.
Can epsilon=1/255 guarantee a 100% success of adversarial attack?

Thanks!

example for providing data from fgsm attack on cifar10 and how to set the output folder for layers.py

How I would set the params for FGSM:

python generate_samples.py -m cifar10 --aa FGSM --gpu 0

and then for layers.py

(detection) user@cerberus16:~/adversarial-detection/expts$ python layers.py --model-type cifar10 --dm proposed  -b 32 -o "layers_test"
Files already downloaded and verified
Files already downloaded and verified
Calculating layer embeddings for the train data:

Number of labeled samples per class:
class 0, count = 5000, proportion = 0.1000
class 1, count = 5000, proportion = 0.1000
class 2, count = 5000, proportion = 0.1000
class 3, count = 5000, proportion = 0.1000
class 4, count = 5000, proportion = 0.1000
class 5, count = 5000, proportion = 0.1000
class 6, count = 5000, proportion = 0.1000
class 7, count = 5000, proportion = 0.1000
class 8, count = 5000, proportion = 0.1000
class 9, count = 5000, proportion = 0.1000

Number of predicted samples per class:
class 0, count = 5000, proportion = 0.1000
class 1, count = 5000, proportion = 0.1000
class 2, count = 5000, proportion = 0.1000
class 3, count = 5000, proportion = 0.1000
class 4, count = 5000, proportion = 0.1000
class 5, count = 5000, proportion = 0.1000
class 6, count = 5000, proportion = 0.1000
class 7, count = 5000, proportion = 0.1000
class 8, count = 5000, proportion = 0.1000
class 9, count = 5000, proportion = 0.1000

Calculating layer embeddings for the test data:

Number of labeled samples per class:
class 0, count = 1000, proportion = 0.1000
class 1, count = 1000, proportion = 0.1000
class 2, count = 1000, proportion = 0.1000
class 3, count = 1000, proportion = 0.1000
class 4, count = 1000, proportion = 0.1000
class 5, count = 1000, proportion = 0.1000
class 6, count = 1000, proportion = 0.1000
class 7, count = 1000, proportion = 0.1000
class 8, count = 1000, proportion = 0.1000
class 9, count = 1000, proportion = 0.1000

Number of predicted samples per class:
class 0, count = 1000, proportion = 0.1000
class 1, count = 1002, proportion = 0.1002
class 2, count = 1003, proportion = 0.1003
class 3, count = 989, proportion = 0.0989
class 4, count = 1016, proportion = 0.1016
class 5, count = 994, proportion = 0.0994
class 6, count = 1007, proportion = 0.1007
class 7, count = 980, proportion = 0.0980
class 8, count = 1014, proportion = 0.1014
class 9, count = 995, proportion = 0.0995

Test set accuracy = 0.9545

Number of layers = 8

Layer: 1
Original dimension = 3072. Train data size = 50000. Sub-sample size used for dimension reduction = 10000
Intrinsic dimensionality: 24

Searching for the best number of neighbors (k) and projected dimension.
INFO:helpers.knn_classifier:Using NPP for dimension reduction.
INFO:helpers.dimension_reduction_methods:Applying PCA as first-level dimension reduction step
INFO:helpers.dimension_reduction_methods:Number of nonzero singular values in the data matrix = 3072
INFO:helpers.dimension_reduction_methods:Number of principal components accounting for 99.5 percent of the data variance = 847
INFO:helpers.dimension_reduction_methods:Dimension of the PCA transformed data = 847
INFO:helpers.dimension_reduction_methods:Dimension of the projected subspace = 240
INFO:helpers.dimension_reduction_methods:Solving the generalized eigenvalue problem to find the optimal projection matrix.
INFO:helpers.knn_classifier:Performing cross-validation to search for the best combination of number of neighbors and projected data dimension:
Traceback (most recent call last):
  File "layers.py", line 308, in <module>
    main()
  File "layers.py", line 291, in main
    search_dimension_and_neighbors(embeddings, labels, indices_sample, model_file, output_file, n_jobs)
  File "layers.py", line 105, in search_dimension_and_neighbors
    n_jobs=n_jobs
  File "adversarial-detection/expts/helpers/knn_classifier.py", line 151, in knn_parameter_search
    labels_test_pred = knn_model.predict_multiple_k(data_proj[ind_te, :], k_range)
  File "adversarial-detection/expts/helpers/knn_classifier.py", line 414, in predict_multiple_k
    nn_indices, nn_distances = self.index_knn.query(X, k=k_list[-1])
  File "adversarial-detection/expts/helpers/knn_index.py", line 235, in query
    return self._query(data, self.index_knn[0], k)
  File "adversarial-detection/expts/helpers/knn_index.py", line 252, in _query
    nn_indices, nn_distances = index.query(data, k=k)
  File "/home/user/.conda/envs/detection/lib/python3.7/site-packages/pynndescent/pynndescent_.py", line 886, in query
    self._init_search_graph()
  File "/home/user/.conda/envs/detection/lib/python3.7/site-packages/pynndescent/pynndescent_.py", line 842, in _init_search_graph
    self._search_graph.transpose()
  File "/home/user/.conda/envs/detection/lib/python3.7/site-packages/scipy/sparse/lil.py", line 437, in transpose
    return self.tocsr(copy=copy).transpose(axes=axes, copy=False).tolil(copy=False)
  File "/home/user/.conda/envs/detection/lib/python3.7/site-packages/scipy/sparse/lil.py", line 462, in tocsr
    _csparsetools.lil_get_lengths(self.rows, indptr[1:])
  File "_csparsetools.pyx", line 109, in scipy.sparse._csparsetools.lil_get_lengths
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)

Adaptive Attacks

Hi, thanks for the sharing codes and it's a solid work. But I have some questions about Adaptive Attacks.
(1) What's your adaptive attacks based on? CW, PGD or FGSM?
(2) I'm more interested in the codes how you can generate adaptive attacks mentioned in your paper. If convenient, could you include the more detailed command line to generate adaptive attacks?

Thanks for your help!

Why is this noisy data generated?

This script generates noisy data?
https://github.com/jayaram-r/adversarial-detection/blob/master/expts/generate_noisy_data.py

Why is this done?
For which defenses is that useful?

Cifar10 Resnet 34 model without activation functions

Why aren't any activation functions in your ResNet34?

Yours... no ReLU

ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (layer1): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (1): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )

Original: with ReLU

ResNet(
  (conv1): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
  )
  (conv2_x): Sequential(
    (0): BasicBlock(
      (residual_function): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (shortcut): Sequential()
    )

data_ty.npy file missing

When I am trying to run your code detection_main.py, it loads the data_ty.npy file. But I can not find this file or some code to generate this file. I appreciate it if any help is provided

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