Bilkent University
Department of Computer Engineering

---

CS 590/690 SEMINAR

 

Analyzing Deceptive Intent: A Multimodal Framework

 

Berat Biçer
Ph.D. Student
(Supervisor: Asst. Prof.Dr. Hamdi Dibeklioğlu)
Computer Engineering Department
Bilkent University

Abstract: In this presentation, we introduce a new approach that utilizes convolutional self-attention for attention-based representation learning, alongside a transformer backbone for transfer learning in our automatic deceit detection framework. Our approach involves analyzing multimodal datapoints by combining visual, vocal, and speech (textual) channels to predict deceptive intent. Experimental results indicate that our architecture surpasses the state-of-the-art on the popular Real-Life Trial (RLT) dataset in terms of correct classification rate. We also assess the generalizability of our approach on the low-stakes Box of Lies (BoL) dataset, achieving state-of-the-art performance and offering cross-corpus insights. Our analysis suggests that convolutional self attention effectively learns meaningful representations and performs joint attention computation for deception. Additionally, we note that apparent deceptive intent appears to be a continuous function of time, with subjects showing varying levels of apparent deceptive intent throughout recordings. Lastly, our findings align with insights from criminal psychology, indicating that studying abnormal behavior out of context may not reliably predict deceptive intent.

 

DATE: March 04, Monday @ 13:50 Place: EA 502