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This is the code we used in our paper Decision Boundary Analysis of Adversarial Examples. They are not especially cleaned.

• gxr3.py: Measure the decision boundary distances and adjacent classes. We are also publishing the randomly chosen directions we used in the Releases page of this repo.
• eval_cg.py: Evaluate images under Cao & Gong's region classification or under point classification.
• optens_attack.py Perform the OPTMARGIN attack (MNIST and CIFAR-10 datasets; attack_v2.py in ImageNet).
• classify.py and classify_test.py: Train a classifier on decision boundary distances and adjacent class data and test the classifier.
• save_orig.py: Extract images and labels from dataset into Numpy files used in some scripts.
• save_correctness.py: Create a compact representation of classification correctness used in some scripts.

For ImageNet, the code expects a copy of research/slim/nets from the TensorFlow models repository in imagenet/nets.