title: Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
abstract: 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.
authors: Nathan Inkawhich Matthew Inkawhich Hai Li Yiran Chen
hIndex: Yiran Chen 51