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Task-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)

Home Page: https://dl.acm.org/citation.cfm?id=3345660

License: MIT License

Python 23.10% Jupyter Notebook 76.90%
deep-convolutional-networks procedural-noise-functions black-box-attacks gabor-noise noise perlin-noise adversarial-attacks adversarial-machine-learning adversarial-examples universal-adversarial-perturbations procedural-noise keras papers

procedural-advml's Issues

utils_attacks.py

If I want to use perlin noise to finish my work , wheather the program statement in perturb function is noise = noise * norm ,or not noise = np.sign((noise - 0.5) * 2) * norm.I think it look like a statement that should be used for making random noise.
I will greatly appreciate a response from you at your earliest convenience.

Use sign function to modify noise

Hi, thank you for the interesting paper and releasing the code.
I notice that there is a line of code in the perturb function:
noise = np.sign(noise) * max_norm
This makes the line in the noise more sharp, right?
I wonder if you have investigated how many benefits this trick gained (in terms of query efficiency, lower max_norm, etc.)? Does this mean higher frequency will help attacking?

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