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This repository has been archived by the owner on Jul 18, 2024. It is now read-only.
Thanks for your method, at the same time, I have a question to ask:
In your code
[_, loc, _] = weibull_min.fit(-np.array(grad_norm_set), c_init, optimizer=scipy_optimizer)#output:shape ,location ,scale
# Compute function value
values = classifier.predict(np.array([x]), logits=True)
value = values[:, pred_class] - values[:, target_class]
# Compute scores
s = np.min([-value[0] / loc, radius])
the parameter called "radius" are set 40, 2, 0.1 in the calculation of L1 norm, L2 norm and inf norm, I want to know whether there is a theoretical basis for these fixed defaults?
Wenrui Guo
The text was updated successfully, but these errors were encountered:
These values you mentioned are the local radius parameters set for L1, L2 and Linfinity perturbation thresholds that are often used in the context of certified defenses in the literature. See an example of Linfinity threshold from https://arxiv.org/abs/1711.00851
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Thanks for your method, at the same time, I have a question to ask:
In your code
the parameter called "radius" are set 40, 2, 0.1 in the calculation of L1 norm, L2 norm and inf norm, I want to know whether there is a theoretical basis for these fixed defaults?
Wenrui Guo
The text was updated successfully, but these errors were encountered: