Is Adversarial Robustness Ready for Prime Time?

Introducing OODRobustBench for Distribution Shift

How robust are machine learning models against attacks? Image created with DALL-E.

Adversarial robustness — the capacity of a machine learning model to withstand carefully crafted attacks — has become a cornerstone of research in reliable artificial intelligence. For years, experts have focused on training algorithms to remain robust when tested on data that resemble what they saw during training. However, real-world data are rarely so predictable. Datasets encountered in practice often differ drastically from those used for training, making it crucial to understand how models hold up against these shifts in input distribution. A new study titled “OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift” tackles precisely this question. It offers a timely reality check on whether today’s “robust” models can truly deliver when tested outside their comfort zone.

This investigation is extremely relevant because distribution shifts happen all the time in real-world scenarios. Medical imaging devices, for instance, might produce images with slightly different characteristics after software updates or under varying scan parameters. Self-driving cars encounter endless unpredictable environmental conditions. Models deployed in security-sensitive applications can confront novel attacks that deviate…