Recently, deception detection on human videos is an eye-catching techniques
and can serve lots applications. AI model in this domain demonstrates the high
accuracy, but AI tends to be a non-interpretable black box. We introduce an
attention-aware neural network addressing challenges inherent in video data and
deception dynamics. This model, through its continuous assessment of visual,
audio, and text features, pinpoints deceptive cues. We employ a multimodal
fusion strategy that enhances accuracy; our approach yields a 92\% accuracy
rate on a real-life trial dataset. Most important of all, the model indicates
the attention focus in the videos, providing valuable insights on deception
cues. Hence, our method adeptly detects deceit and elucidates the underlying
process. We further enriched our study with an experiment involving students
answering questions either truthfully or deceitfully, resulting in a new
dataset of 309 video clips, named ATSFace. Using this, we also introduced a
calibration method, which is inspired by Low-Rank Adaptation (LoRA), to refine
individual-based deception detection accuracy.

通过注意力感知神经网络和多模态融合策略，我们提出的方法在视频数据中准确地检测和阐明欺骗行为， 进一步使用低秩适应方法进行个体欺骗检测准确率的提高。