We propose a stealthy clean-label video backdoor attack against Deep Learning
(DL)-based models aiming at detecting a particular class of spoofing attacks,
namely video rebroadcast attacks. The injected backdoor does not affect
spoofing detection in normal conditions, but induces a misclassification in the
presence of a specific triggering signal. The proposed backdoor relies on a
temporal trigger altering the average chrominance of the video sequence. The
backdoor signal is designed by taking into account the peculiarities of the
Human Visual System (HVS) to reduce the visibility of the trigger, thus
increasing the stealthiness of the backdoor. To force the network to look at
the presence of the trigger in the challenging clean-label scenario, we choose
the poisoned samples used for the injection of the backdoor following a
so-called Outlier Poisoning Strategy (OPS). According to OPS, the triggering
signal is inserted in the training samples that the network finds more
difficult to classify. The effectiveness of the proposed backdoor attack and
its generality are validated experimentally on different datasets and
anti-spoofing rebroadcast detection architectures.

我们提出了一种针对深度学习模型的隐蔽干净标签视频后门攻击，旨在检测特定类别的欺诈攻击，包括视频重新广播攻击。该攻击不会影响正常情况下的欺诈检测，但在特定触发信号的存在下，会产生错误分类。