Another alternative to the regular Lp-constrained adversarial examples that is additionally less visible than adversarial patches or frames are adversarial transformations such as small crops, rotations and translations. Similar to Lp adversarial examples, adversarial transformations are often less visible unless the original image is available for direct comparison. In this article, I will include a PyTorch implementation and some results against adversarial training.
Adversarial patches and frames are an alternative to the regular $L_p$-constrained adversarial examples. Often, adversarial patches are thought to be more realistic — mirroring graffitis or stickers in the real world. In this article I want to discuss a simple PyTorch implementation and present some results of adversarial patches against adversarial training as well as confidence-calibrated adversarial training.
Out-of-distribution examples are images that are cearly irrelevant to the task at hand. Unfortunately, deep neural networks frequently assign random labels with high confidence to such examples. In this article, I want to discuss an adversarial way of computing high-confidence out-of-distribution examples, so-called distal adversarial examples, and how confidence-calibrated adversarial training handles them.