Adversarial Examples in Machine Learning

Machine learning models, including deep neural networks, were shown to be vulnerable to adversarial examples—subtly (and often humanly indistinguishably) modified malicious inputs crafted to compromise the integrity of their outputs. Adversarial examples thus enable adversaries to manipulate system behaviors. Potential attacks include attempts to control the behavior of vehicles, have spam content identified as legitimate content, or have malware identified as legitimate software.

In fact, the feasibility of misclassification attacks based on adversarial examples has been shown for image, text, and malware classifiers. Furthermore, adversarial examples that affect one model often affect another model, even if the two models are very different. This effectively enables attackers to target remotely hosted victim classifiers with very little adversarial knowledge.

This talk covers adversarial example crafting algorithms operating under varying threat models and application domains, as well as defenses proposed to mitigate such attacks. A practical tutorial will be given throughout the talk, allowing participants to familiarize themselves with adversarial example crafting.