Camera Adversaria

In this paper we introduce Camera Adversaria; a mobile app designed to disrupt the automatic surveillance of personal photographs by technology companies. The app leverages the brittleness of deep neural networks with respect to high-frequency signals, adding generative adversarial perturbations to users' photographs. These perturbations confound image classification systems but are virtually imperceptible to human viewers. Camera Adversaria builds on methods developed by machine learning researchers as well as a growing body of work, primarily from art and design, which transgresses contemporary surveillance systems. We map the design space of responses to surveillance and identify an under-explored region where our project is situated. Finally we show that the language typically used in the adversarial perturbation literature serves to affirm corporate surveillance practices and malign resistance. This raises significant questions about the function of the research community in countenancing systems of surveillance.