  Adversarial attacks pose a threat to deep learning models. However, research
on adversarial detection methods, especially in the multi-modal domain, is very
limited. In this work, we propose an efficient and straightforward detection
method based on the temporal correlation between audio and video streams. The
main idea is that the correlation between audio and video in adversarial
examples will be lower than benign examples due to added adversarial noise. We
use the synchronisation confidence score as a proxy for audiovisual correlation
and based on it we can detect adversarial attacks. To the best of our
knowledge, this is the first work on detection of adversarial attacks on
audiovisual speech recognition models. We apply recent adversarial attacks on
two audiovisual speech recognition models trained on the GRID and LRW datasets.
The experimental results demonstrate that the proposed approach is an effective
way for detecting such attacks.
