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

LoRA-like Calibration for Multimodal Deception Detection using ATSFace Data