Automatic Deception Detection has been a hot research topic for a long time,
using machine learning and deep learning to automatically detect deception,
brings new light to this old field. In this paper, we proposed a voting-based
method for automatic deception detection from videos using audio, visual and
lexical features. Experiments were done on two datasets, the Real-life trial
dataset by Michigan University and the Miami University deception detection
dataset. Video samples were split into frames of images, audio, and
manuscripts. Our Voting-based Multimodal proposed solution consists of three
models. The first model is CNN for detecting deception from images, the second
model is Support Vector Machine (SVM) on Mel spectrograms for detecting
deception from audio and the third model is Word2Vec on Support Vector Machine
(SVM) for detecting deception from manuscripts. Our proposed solution
outperforms state of the art. Best results achieved on images, audio and text
were 97%, 96%, 92% respectively on Real-Life Trial Dataset, and 97%, 82%, 73%
on video, audio and text respectively on Miami University Deception Detection.

采用多模态投票型方法，利用图像、声音和语言特征进行自动欺骗检测，实验结果表明该方法优于现有技术。

基于投票的多模态自动欺诈检测

Voting-based Multimodal Automatic Deception Detection

Automatic deception detection is an important task that has gained momentum
in computational linguistics due to its potential applications. In this paper,
we propose a simple yet tough to beat multi-modal neural model for deception
detection. By combining features from different modalities such as video,
audio, and text along with Micro-Expression features, we show that detecting
deception in real life videos can be more accurate. Experimental results on a
dataset of real-life deception videos show that our model outperforms existing
techniques for deception detection with an accuracy of 96.14% and ROC-AUC of
0.9799.

本文提出了一种简单却难以超越的多模态神经模型，通过结合来自不同模态的特征，例如视频、音频、文本和微表情，我们证明了在现实生活视频中检测欺骗可以更加准确。在真实生活欺骗视频数据集上的实验结果表明，我们的模型在欺骗检测方面的准确率达到了 96.14％，ROC-AUC 为 0.9799。