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| Title: | 多模態融合用於欺騙偵測
Multimodal Fusion for Deception Detection |
| Authors: | 孫振原
Sun, Cheng-Yuan |
| Contributors: | 蕭舜文
Hsiao, Shun-Wen
孫振原
Sun, Cheng-Yuan |
| Keywords: | 多模態
欺騙偵測
注意力機制
集成學習
模型校正
Multimodal
Deception detection
Attention mechanism
Ensemble
Calibration |
| Date: | 2023 |
| Issue Date: | 2023-09-01 14:54:51 (UTC+8) |
| Abstract: | 現今，各種影片類型的資料如雨後春筍般冒出。在以人為中心的影片分析領域中，欺騙偵測已成為我們面臨的重要議題。儘管基於人工智慧的欺騙偵測模型已取得了驚人的準確度，但它們往往缺乏可解釋性並作為一個「黑箱」運作。在本研究中，我們提出了一種基於注意力機制的神經網絡來進行欺騙偵測，解決可變長度的影片資料和欺騙過程所帶來的的挑戰。我們的模型不斷評估視覺、音頻和文本訊息，精確地找出顯露欺騙跡象的時刻。此外，我們採用了多模態方法，在具有不同特徵的多個模型之後加入了融合機制，使協同推理在欺騙偵測上能夠實現更為精確及全面。在真實審判資料集上，我們的模型達到了92％的準確度，多模態融合的表現優於單模態方法。該模型可同時輸出每一時刻的注意力權重，從而為包含潛在欺騙線索的特定時間間隔提供有價值的見解。因此，我們的方法不僅在檢測欺騙方面表現出色，還能讓人了解欺騙過程中的動態變化，使其成為多種情境中的有力工具。此外，我們還進行了一項實驗，請學校學生對有關個人事務的問題做出誠實或欺騙性的回答。資料集由309個影片片段組成，誠實和欺騙性回答各佔一半，並使用自動語音識別建立詳細文字記錄。我們還設計了一個類似於LoRA的模型，針對個體校準模型，從而提高對未見個體的準確性。
Nowadays, various video data are springing up. In the field of human-centric video analysis, deception detection becomes a crucial issue to us. Although AI-based deception detection models have achieved remarkable accuracy, they often lack interpretability and work as a "black box." In this study, we propose an attention-aware neural network for deception detection, addressing the challenges of variable-length video data and the process of deception. Our model continuously assesses visual, audio, and textual information, pinpointing moments revealing signs of deception. Additionally, we embrace a multimodal approach, incorporating a fusion mechanism following multiple models with distinct features, enabling collaborative inference for more accurate and comprehensive deception detection. Our model achieves 92\\% accuracy on a real-life trials dataset, with the multimodal ensemble outperforming unimodal approaches. The model simultaneously outputs attention weights for each moment, providing valuable insights into specific time intervals containing potential deception cues. Thus, our approach not only excels at detecting deception but also offers an understanding of the dynamics underlying deception, making it a powerful tool in a variety of contexts. Moreover, we conducted an experiment involving university students who were asked to respond truthfully or deceptively to questions about personal matters. The dataset consists of 309 video clips, equally divided between truthful and deceptive responses, supplemented with detailed transcripts created through automatic speech recognition. We also design a LoRA-like model to calibrate the model by individuals, improving accuracy for unseen individuals. |
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| Description: | 碩士
國立政治大學
資訊管理學系
110356041 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110356041 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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