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why the adversarial perturbations was damaged by saving the adversarial samples with scipy.misc. #3
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Discretizing the values from a real-numbered value to one of the 256 points degrades the quality of the adversarial examples. This can easily be fixed by performing a second optimization step on the lattice of discretized images (often only a few iterations is necessary). However, if you don't want to have to do this, you can also just save and load it as float32. |
@carlini Actually, the model used in your 'setup_mnist.py' file was trained based on cleverhans library. |
Sorry, I'm not sure what you're trying to say. Are you attacking and defending using the same model? If they are different, you will need to generate transferable adversarial examples, to do this set the confidence to 3 or 4 on MNIST. I'm not sure what this has to do with cleverhans. |
Hello, @carlini . Sorry to bother you again. I'm try to defense your attack. But there is a strange thing happened: the adversarial perturbations was damaged by saving the adversarial samples with scipy.misc.
And the image is dealed with ' image = image / 255.0 - 0.5 ' when as the input of pre-trained model
and We find the final output is different with the start.
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