Abstract: With the rapid development of deep learning,neural networks are widely used in various fields.However,neural networks still face the problem of adversarial attacks.Among all types of adversarial attacks,the boundary black-box attack can only obtain the final classification label of the tested model,so it is closest to the actual application scenario,and is recognized as the most practical and difficult attacks,which has attracted more and more researchers to conduct related research.Nevertheless,current relevant research mainly focus on image recognition models,and with less research on video recognition models.To this end,this paper proposes a boundary black-box video adversarial example generation algorithm BBVA.BBVA uses a progressive exploration mechanism to generate adversarial videos,which effectively improves the efficiency of updating samples.Experiments show that compared with the state-of-the-art boundary black-box video adversarial example generation algorithm STDE,BBVA better balances the noise size and model queries,and gets the best results in this research field in many measurement indicators such as visual effect,optimization distance and fooling rate.In addition,under more severe conditions,BBVA even outperforms some state-of-the-art score-based black-box video adversarial example generation algorithms,such as EARL and VBAD.The proposed algorithm can be used to provide adversarial training samples to enhance video model security.

Key words: Adversarial example,Video recognition,Boundary,Black-box,Neural networks