  The success of deep learning research has catapulted deep models into
production systems that our society is becoming increasingly dependent on,
especially in the image and video domains. However, recent work has shown that
these largely uninterpretable models exhibit glaring security vulnerabilities
in the presence of an adversary. In this work, we develop a powerful untargeted
adversarial attack for action recognition systems in both white-box and
black-box settings. Action recognition models differ from image-classification
models in that their inputs contain a temporal dimension, which we explicitly
target in the attack. Drawing inspiration from image classifier attacks, we
create new attacks which achieve state-of-the-art success rates on a two-stream
classifier trained on the UCF-101 dataset. We find that our attacks can
significantly degrade a model's performance with sparsely and imperceptibly
perturbed examples. We also demonstrate the transferability of our attacks to
black-box action recognition systems.
