  This work demonstrates a physical attack on a deep learning image
classification system using projected light onto a physical scene. Prior work
is dominated by techniques for creating adversarial examples which directly
manipulate the digital input of the classifier. Such an attack is limited to
scenarios where the adversary can directly update the inputs to the classifier.
This could happen by intercepting and modifying the inputs to an online API
such as Clarifai or Cloud Vision. Such limitations have led to a vein of
research around physical attacks where objects are constructed to be inherently
adversarial or adversarial modifications are added to cause misclassification.
Our work differs from other physical attacks in that we can cause
misclassification dynamically without altering physical objects in a permanent
way.
  We construct an experimental setup which includes a light projection source,
an object for classification, and a camera to capture the scene. Experiments
are conducted against 2D and 3D objects from CIFAR-10. Initial tests show
projected light patterns selected via differential evolution could degrade
classification from 98% to 22% and 89% to 43% probability for 2D and 3D targets
respectively. Subsequent experiments explore sensitivity to physical setup and
compare two additional baseline conditions for all 10 CIFAR classes. Some
physical targets are more susceptible to perturbation. Simple attacks show near
equivalent success, and 6 of the 10 classes were disrupted by light.
