Comments (1)
Unfortunately we are not actively maintaining this project and we have thus not tested it with recent versions of tensorflow. I think your best bet is to use the versions that were standard during the development of the code, that is Summer 2017.
At the same time, you might be interested in the robustness library which is more recent and contains all the relevant code for training and evaluating adversarially robust models.
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Related Issues (16)
- Release Model HOT 3
- wrong implementation of CW attack HOT 3
- Any adversarial attack that sustains after resize attack? HOT 3
- What does this line do? HOT 1
- Why epsilon can be larger than 1? HOT 6
- Adversarial performance for MNIST models vary widely with different random seed initializations HOT 1
- Requirements to reproduce the results
- Are there any Pytorch version of this challenge? Cause tensorflow usually has conflict with my Pytorch. HOT 2
- (Not an issue) Request to keep the challenge open HOT 2
- Does PGD not need to perform random restart in every iterative ? Is it enough to start with random noise at FGSM? HOT 3
- Question about reproducing your results HOT 2
- For the reported results with 100 iterations, is the eps_iter/"a" value still 0.01? HOT 1
- About random restart HOT 2
- parameters for train robust adv_trained/secret networks? HOT 1
- tf.tf.Variable() seems cannot be replaced with tf.get_variable(). HOT 8
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