Frank R. Schmidt

Recent work has thoroughly documented the susceptibility of deep learning systems to adversarial examples, but most such instances directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in all cases these involve manipulating the object of interest, e.g., putting a physical sticker on a object to misclassify it, or manufacturing an object specifically intended to be misclassified.
In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself? We show that this is indeed possible, that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet reliably misclassify target objects as a different (targeted) class. To accomplish this, we propose an iterative procedure for both updating the attack perturbation (to make it adversarial for a given classifier), and the threat model itself (to ensure it is physically realizable).
For example, we show that we can achieve physically-realizable attacks that fool ImageNet classifiers in a targeted fashion 49.6% of the time. This presents a new class of physically-realizable threat models to consider in the context of adversarially robust machine learning.

This paper is also stored on arXiv.

@InProceedings\{LSK19,
  author       = "Li, Juncheng B. and Schmidt, Frank R. and Kolter, J. Zico",
  title        = "Adversarial camera stickers: A physical camera-based attack on deep learning systems",
  booktitle    = "International Conference on Machine Learning (ICML)",
  month        = "Jun",
  year         = "2019",
  publisher    = "PMLR",
  url          = "http://frank-r-schmidt.de/Publications/2019/LSK19"
}