Role of psychological factors in machine learning-based deception detection
Deception detection technology has been researched in various fields such as law enforcement, border controls and psychology. There is a rising in demand for a machine learning solution that is able to detect deceptions in videos. One of the reasons for this demand is the increase of false informati...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/167138 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | Deception detection technology has been researched in various fields such as law enforcement, border controls and psychology. There is a rising in demand for a machine learning solution that is able to detect deceptions in videos. One of the reasons for this demand is the increase of false information circulating online. The objective of this project is to tackle current problems with deception detection using a multidisciplinary approach. The report investigates the role of psychological factors in machine learning-based deception detection. Baseline evaluation is tested on publicly available datasets and ROSE Lab dataset, providing accuracy results that are supported with concepts of psychological theories. Findings of this research suggests that additional modalities such as verbal and gaze modality can be fused with visual and vocal modalities to boost the accuracy of deception detection. Additionally, different types of approaches such as cognitive load theory, expectancy violation theory and cognitive dissonance theory are explored to form interview strategies that are able to enhance the data collection process. |
|---|