Comments (2)
Hi,
for randomized defenses the main difference is using APGD with Expectation over Transformations (EoT), and using the untargeted losses. To set the number of estimations of the gradient over which taking the average, one can modify the value of eot_iter
here (we use 20 in the paper).
I will add a specific option to set this automatically as soon as possible. Temporarily, if you initialize the attack with version='custom'
and then add
adversary.attacks_to_run = ['apgd-ce', 'apgd-dlr']
adversary.apgd.n_restarts = 1
adversary.apgd.eot_iter = 20
you should get the evaluation with EoT. The modification to use Square for randomized defenses is not included in the code at the moment, but in our experiments APGD is anyway the most effective attack in this case.
Let me know if this works for you!
from auto-attack.
Sounds great! Thank you.
from auto-attack.
Related Issues (20)
- Can AutoAttack be used to dense prediction task? HOT 2
- Softmax probabilities instead of logits HOT 3
- normalization with deepfool fmodel HOT 4
- About `n_iter`, to align with others HOT 4
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- 4 classes model; targeted attacks are different HOT 2
- A Bug in Square Attack HOT 4
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- Unable to use Python debugger HOT 1
- Unable to use in TF2 HOT 5
- Parallelized computing HOT 5
- Invalid configuration for square attack HOT 2
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from auto-attack.