When data is publicly released for human consumption, it is unclear how to
prevent its unauthorized usage for machine learning purposes. Successful model
training may be preventable with carefully designed dataset modifications, and
we present a proof-of-concept approach for the image classification setting. We
propose methods based on the notion of adversarial shortcuts, which encourage
models to rely on non-robust signals rather than semantic features, and our
experiments demonstrate that these measures successfully prevent deep learning
models from achieving high accuracy on real, unmodified data examples.

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