Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. With the recent uprising of adversarial attack which automatically and strategically look to computing data pertubation in order to fool well-trained classifiers, this project looks into the vulnerability of existing classifiers against adversarial attack and how to improve their resistence and robustness.