Practical protection against video data leakage via universal adversarial head

Abstract

While online video sharing becomes more popular, it also causes unconscious leakage of personal information in the video retrieval systems like deep hashing. A snoop can collect more users’ private information from the video database by querying similar videos. This paper focuses on bypassing the deep video hashing based retrieval to prevent information from being maliciously collected. We propose universal adversarial head (UAH), which crafts adversarial query videos by prepending the original videos with a sequence of adversarial frames to perturb the normal hash codes in the Hamming space. This adversarial head can be generated only with a few natural videos, and mislead the retrieval system to return irrelevant videos when it is applied to most query videos. Furthermore, to obey the principle of information protection, we expand the proposed method to a data-free paradigm to generate the UAH, without access to users’ original videos. Extensive experiments demonstrate the effectiveness of our method in misleading deep video hashing under both white-box and black-box settings.