Comments (3)
I am not aware of any such attack but it should be possible in principle. Since up/down-scaling images is a differentiable operation, one can incorporate it directly into PGD.
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So the idea would be add a layer between the input and the first network layer that performs this resize attack (lo -> hi -> lo def). Then attack this augmented network with a standard method (PGD). The adversarial example you find will be adversarial even after resizing.
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Related Issues (16)
- Release Model HOT 3
- wrong implementation of CW 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
- about version HOT 1
- (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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