adversarial-patch
PyTorch implementation of adversarial patch
This is an implementation of the Adversarial Patch paper. Not official and likely to have bugs/errors.
How to run:
Data set-up:
Run attack:
Results:
Using patch shapes of both circles and squares gave good results (both achieved 100% success on the training set and eventually > 90% success on test set)
I managed to recreate the toaster example in the original paper. It looks slightly different but it is evidently a toaster.
This is a toaster
Square patches are a little more homogenous due to that I only rotate by multiples of 90 degrees.
This is also a toaster
Issues:
Cannot make a perfect circle with numpy/pytorch. The hack I came up with makes the boundary slightly hexagonal.
Rather slow if max_count and conf_target are large.
Probably lots of redundant calls and variables.