论文标题
暂时性的镀铬触发器，用于针对反磁带重新广播检测的清洁标签后门攻击
A temporal chrominance trigger for clean-label backdoor attack against anti-spoof rebroadcast detection
论文作者
论文摘要
我们提出了针对深度学习（DL）的模型，旨在检测特定类别的欺骗攻击，即视频重新播放攻击，对基于深度学习的模型提出了隐秘的清洁标签视频后门攻击。注射后门不会影响正常条件下的欺骗检测，而是在存在特定触发信号的情况下引起错误分类。所提出的后门依赖于时间触发，从而改变了视频序列的平均色彩。后门信号是通过考虑人类视觉系统（HVS）的特殊性来设计的，以降低扳机的可见性，从而增加后门的隐身性。为了迫使网络在具有挑战性的清洁标签场景中查看触发器的存在，我们选择了在所谓的离群中毒策略（OPS）后，选择用于注射后门的中毒样品。根据OPS的说法，触发信号插入了网络发现更难分类的训练样本中。拟议的后门攻击及其普遍性的有效性在不同的数据集和反欺骗性重新广播检测体系中进行了实验验证。
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.
