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We show that by controlling parts of a physical environment in which apre-trained deep neural network (DNN) is being fine-tuned online, an adversarycan launch subtle data poisoning attacks that degrade the performance of thesystem. While the attack can be applied in general to any perception task, weconsider a DNN based traffic light classifier for an autonomous car that hasbeen trained in one city and is being fine-tuned online in another city. Weshow that by injecting environmental perturbations that do not modify thetraffic lights themselves or ground-truth labels, the adversary can cause thedeep network to learn spurious concepts during the online learning phase. Theattacker can leverage the introduced spurious concepts in the environment tocause the model's accuracy to degrade during operation; therefore, causing thesystem to malfunction.

Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems