# Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks Source: [Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:18:37.363Z Authors: Matthew Ciolino, Josh Kalin, David Noever Published: 2021-02-19T00:47:16.000Z ## Abstract Production machine learning systems are consistently under attack by adversarial actors. Various deep learning models must be capable of accurately detecting fake or adversarial input while maintaining speed. In this work, we propose one piece of the production protection system: detecting an incoming adversarial attack and its characteristics. Detecting types of adversarial attacks has two primary effects: the underlying model can be trained in a structured manner to be robust from those attacks and the attacks can be potentially filtered out in real-time before causing any downstream damage. The adversarial image classification space is explored for models commonly used in transfer learning. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()