ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

Understand

Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it.

• However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leading to suboptimal robustness evaluations.
• To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches.
• It consists of a set of patches, optimized to generalize across different models, and readily applicable to ImageNet data after preprocessing them with affine transformations.