Comments (2)
Hi bdseal,
We already present results on these datasets in our paper: on Madry and TRADES models in Table 4 (MNIST) and on CLP and LSQ models in Table 6 (MNIST and CIFAR-10). Although in these experiments we focus only on untargeted attacks.
If you would like to evaluate Square Attack on some other models or additionally benchmark query efficiency, please check our instructions on how to run our code: https://github.com/max-andr/square-attack#running-the-code. It's really simple:
python attack.py --attack=square_linf --model=madry_mnist_robust --n_ex=10000 --eps=0.3 --p=0.8 --n_iter=20000
And you can add the --targeted
flag to make the attack targeted (against a randomly chosen target class).
I hope that helps!
Best,
Maksym
from square-attack.
thanks a lot.
from square-attack.
Related Issues (13)
- Where does this paper published on? HOT 4
- L2 targeted attack taking very much query with low success rate HOT 2
- Bugs when running Linf attack on pt_inception HOT 4
- 1.Hi, I would like to know what do you refer to as Rademacher distribution in A.4, and why HOT 2
- Why the value of epsilon in step 4 in Algorithm 2 needs to be multiplied by 2? HOT 3
- Can you provide the code of defensive model in your paper's section 5.2 for me? HOT 6
- Why do you use two windows in L2 norm attack? I don't understand the mass moving (Fig.2) HOT 10
- Why the loss type is margin_loss in untargeted attack, but cross_entropy loss in targeted attack. HOT 2
- Why the perturbation with its value equals the maximum bound of Linf and L2 attack should be used in update? HOT 4
- The Linf version HOT 2
- FileNotFoundError: '/scratch/maksym/imagenet/val_orig' HOT 1
- Why L2 norm attack samples the same window over one-batch's images? HOT 5
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