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Code for "Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes"

License: MIT License

Python 100.00%

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plpl2019 eric-vader

bayes_attack's Issues

hello,How to rewrite torch.irfft to the new version in line 80 of the fft_transform_mc function in utils.py?

bug report, why new_x = candidates[0].detach()?

In https://github.com/satyanshukla/bayes_attack/blob/main/attack.py#L102
The code of new_x = candidates[0].detach() would cause transform error!
because shape of candidates is [1, 1944].
Because the pert does not have axis=1(dim=1) in the statement of t_dim = int(np.sqrt(pert.shape[1]/(2*channel))). (https://github.com/satyanshukla/bayes_attack/blob/main/utils.py#L70)

Bug report, when I run this code on ImageNet, it reports warning and GPU memory is exhausted (OOM error)!

It seems the bayes does not work, because after long time, it seems that it cannot attack successfully even after many iterations.
It reports:

warnings.py:110] /home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/distributions/multivariate_normal.py:230: NumericalWarning: Negative variance values detected. This is likely due to numerical instabilities. Rounding negative variances up to 1e-06.
  NumericalWarning,

And then

    adv_images, query, distortion_with_max_queries, success = self.bayes_opt(batch_index, images, true_labels[0].item(), args)
  File "bayes_attack/attack.py", line 193, in bayes_opt
    new_x, new_obj = self.optimize_acqf_and_get_observation(qEI, x0, y0)
  File "bayes_attack/attack.py", line 113, in optimize_acqf_and_get_observation
    raw_samples=200,
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/botorch/optim/optimize.py", line 376, in joint_optimize
    sequential=False,
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/botorch/optim/optimize.py", line 150, in optimize_acqf
    options=options,
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/botorch/optim/initializers.py", line 104, in gen_batch_initial_conditions
    X_rnd[start_idx:end_idx].to(device=device)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/botorch/utils/transforms.py", line 171, in decorated
    return method(cls, X)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/botorch/acquisition/analytic.py", line 137, in forward
    posterior = self._get_posterior(X=X)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/botorch/acquisition/analytic.py", line 69, in _get_posterior
    posterior = self.model.posterior(X)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/botorch/models/gpytorch.py", line 276, in posterior
    mvn = self(X)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/models/exact_gp.py", line 319, in __call__
    predictive_mean, predictive_covar = self.prediction_strategy.exact_prediction(full_mean, full_covar)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/models/exact_prediction_strategies.py", line 317, in exact_prediction
    self.exact_predictive_mean(test_mean, test_train_covar),
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/models/exact_prediction_strategies.py", line 335, in exact_predictive_mean
    res = (test_train_covar @ self.mean_cache.unsqueeze(-1)).squeeze(-1)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/lazy/lazy_tensor.py", line 1889, in __matmul__
    return self.matmul(other)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/lazy/lazy_tensor.py", line 1117, in matmul
    return func.apply(self.representation_tree(), other, *self.representation())
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/lazy/lazy_evaluated_kernel_tensor.py", line 316, in representation_tree
    return self.evaluate_kernel().representation_tree()
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/utils/memoize.py", line 59, in g
    return _add_to_cache(self, cache_name, method(self, *args, **kwargs), *args, kwargs_pkl=kwargs_pkl)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/lazy/lazy_evaluated_kernel_tensor.py", line 274, in evaluate_kernel
    res = self.kernel(x1, x2, diag=False, last_dim_is_batch=self.last_dim_is_batch, **self.params)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/kernels/kernel.py", line 396, in __call__
    res = lazify(super(Kernel, self).__call__(x1_, x2_, last_dim_is_batch=last_dim_is_batch, **params))
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/module.py", line 28, in __call__
    outputs = self.forward(*inputs, **kwargs)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/kernels/scale_kernel.py", line 92, in forward
    orig_output = self.base_kernel.forward(x1, x2, diag=diag, last_dim_is_batch=last_dim_is_batch, **params)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/kernels/matern_kernel.py", line 102, in forward
    distance = self.covar_dist(x1_, x2_, diag=diag, **params)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/kernels/kernel.py", line 329, in covar_dist
    res = self.distance_module._dist(x1, x2, postprocess, x1_eq_x2)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/kernels/kernel.py", line 54, in _dist
    res = self._sq_dist(x1, x2, postprocess=False, x1_eq_x2=x1_eq_x2)
  File "/home/yiyangzhao/anaconda3/lib/python3.7/site-packages/gpytorch/kernels/kernel.py", line 43, in _sq_dist
    res = x1_.matmul(x2_.transpose(-2, -1))
RuntimeError: CUDA out of memory. Tried to allocate 1.55 GiB (GPU 0; 10.76 GiB total capacity; 6.55 GiB already allocated; 1.39 GiB free; 8.11 GiB reserved in total by PyTorch)

I guess this is because the train_x is always added new one. So gpu memory is exhausted.

RuntimeError

Your code uses the function torch.symeig(), and sometimes it will get RuntimeError: symeig_cpu: the algorithm failed to converge; 2 off-diagonal elements of an intermediate tridiagonal form did not converge to zero. Did you come across this problem? How can I fix it?

Dataset: ImageNet ILSVRC2012_val
Model: inception_v3, resnet18, resnet101 and densenet121.

Code for targeted attack

Hello,
Nicely written paper and code! One query, I couldn't find the code for targeted attack? Could someone please point me towards it?

Regards.

hello，How to rewrite torch.irfft to the new version of torch in line 80 of the fft_transform_mc function in utils.py？

Reproduce Issues

Hi, your article intrigues me and I am amazed at the huge reduction in query costs. However I am having a bit of trouble reproducing it and need your help. According to the experiments described in the article, the CIFAR10 dataset uses infinite norms, so its low resolution subspace should correspond to nearest neighbor interpolation (NNI), however I didn't find this function in your code. In addition, the experimental section only gives the eps parameter for CIFAR10, the rest of the parameter settings are still unknown. I would appreciate if you could reply me when you have time!

Why fit_gpytorch_model does not use new data in different iterations?

In each iteration, the same mll is used in fit_gpytorch_model(mll), which means the new_x and new_obj is not used.
It is located in https://github.com/satyanshukla/bayes_attack/blob/main/attack.py#L131
Why?

How to support targeted attack?

How to modify this code to support targeted attack using target class label?

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