LDNet: A Robust Hybrid Approach for Lie Detection Using Deep Learning Techniques

Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions.The human face is a rich source of data that offers trustworthy markers of deception.The deception or lie detection systems are non-intrusive,cost-effective,and mobile by identifying facial expressions.Over the last decade,numerous studies have been conducted on deception detection using several advanced techniques.Researchers have focused their attention on inventing more effective and efficient solutions for the detection of deception.So,it could be challenging to spot trends,practical approaches,gaps,and chances for contribution.However,there are still a lot of opportunities for innovative deception detection methods.Therefore,we used a variety of machine learning(ML)and deep learning(DL)approaches to experiment with this work.This research aims to do the following:(i)review and analyze the current lie detection(LD)systems;(ii)create a dataset;(iii)use several ML and DL techniques to identify lying;and(iv)create a hybrid model known as LDNet.By combining layers from Vgg16 and DeneseNet121,LDNet was developed and offered the best accuracy(99.50%)of all the models.Our developed hybrid model is a great addition that significantly advances the study of LD.The findings from this research endeavor are expected to advance our understanding of the effectiveness of ML and DL techniques in LD.Furthermore,it has significant practical applications in diverse domains such as security,law enforcement,border control,organizations,and investigation cases where accurate lie detection is paramount.

A Deep Learning Model for EEG-Based Lie Detection Test Using Spatial and Temporal Aspects

Lie detection test is highly significant task due to its impact on criminology and society.Computerized lie detection test model using electroencephalogram(EEG)signals is studied in literature.In this paper we studied deep learning framework in lie detection test paradigm.First,we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole image.Our model describes a temporal feature map of the EEG signals measured during the lie detection test.A deep learning attention model(V-TAM)extracts the temporal map vector during the learning process.This technique reduces computational time and lessens the overfitting in Deep Learning architectures.We propose a Cascading attention model with a deep learning convolutional neural network(CNN).V-TAM model extracts local features and global features in separate paths spatial and temporal.Also,to enhance the EEG segmentation precision,a novel Visual-Temporal AttentionModel(V-TAM)is proposed.The accuracy was evaluated using data measured from a sensor from a public dataset of 9512 subjects during fifteen minutes lie detection task.We compared our model with three recent published models.Our proposed model attained the highest performance of(98.5%)with(p<0.01).The visual-temporal model of the proposed platform shows an optimized balance between prediction accuracy and time efficiency.Validation investigationwere performed to prove the correctness and reliability of the proposed method through sizing of the input data,proving its effectiveness in attaining satisfactory performance by using only a smaller size input data.