A Deep Learning Approach for Multimodal
Deception Detection
Gangeshwar Krishnamurthy1 , Navonil Majumder2 , Soujanya Poria3 , and Erik
Cambria4

arXiv:1803.00344v1 [cs.CL] 1 Mar 2018

1

A*STAR Artificial Intelligence Initiative (A*AI),
Institute of High Performance Computing, Singapore
gangeshwark@ihpc.a-star.edu.sg
2
Centro de Investigacin en
Computacin, IPN, Mexico
navonil@sentic.net
3
Temasek Laboratories,
Nanyang Technological University, Singapore
sporia@ntu.edu.sg
4
School of Computer Science and Engineering,
Nanyang Technological University, Singapore
cambria@ntu.edu.sg

Abstract. 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.

1

Introduction

We face deceptive behavior in our day-to-day life. People lie to escape from a
situation that seems unfavorable to them. As a consequence, some lies are innocuous but others may have severe ramifications in the society. Reports suggest
that the ability of humans to detect deception without special aids is only 54%[1].
A study by DePaulo et al. [2] found that deception without any particular motivation or intention exhibited almost no detectable cues of deception. However,
cues were significantly more when lies were about transgressions.
With the rise in the number of criminal cases filed every year in the US5 , it
is ethically and morally important to accuse only the guilty defendant and free
the innocent. Since the judgment for any case is mostly based on the hearings
and evidence from the stakeholders (accused, witnesses, etc.), the judgment is
5

http://www.uscourts.gov

most likely to go wrong if the stakeholders do not speak the truth. It is, hence,
important to detect deceptive behavior accurately in order to upkeep the law
and order.
Social media can be characterized as a virtual world where people interact
with each other without the human feel and touch. It is easy to not reveal one’s
identity and/or pretend to be someone else on the social media. Cyberbullying is
increasingly becoming a common problem amongst the teenagers nowadays [3].
These include spreading rumors about a person, threats, and sexual harassment.
Cyberbullying adversely affects the victim and leads to a variety of emotional
responses such as lowered self-esteem, increased suicidal thoughts, anger, and
depression[4]. Teenagers fall prey to these attacks due to their inability to comprehend the chicanery and pretentious behavior of the attacker.
Another area where deception detection is of paramount importance is with
the increased number of false stories, a.k.a Fake News, on the Internet. Recent
reports suggest that the outcome of the U.S. Presidential Elections is due to
the rise of online fake news. Propagandists use arguments that, while sometimes
convincing, are not necessarily valid. Social media, such as Facebook and Twitter, have become the propellers for this political propaganda. Countries around
the world, such as France [5], are employing methods that would prevent the
spread of fake news during their elections. Though these measures might help,
there is a pressing need for the computational linguistics community to devise
efficient methods to fight Fake News given that humans are poor at detecting
deception.
This paper is organized as follows. In section 2, we will talk about the past
work in deception detection; section 3 describes our approach to solving deception detection. Section 4 explains our experimental setup. In section 5 and 6, we
discuss our results and drawbacks respectively. And finally, conclude with future
work in section 7.

2

Related Work

Past research in the detection of deception can be broadly classified as Verbal
and Non-verbal. In verbal deception detection, the features are based on the
linguistic characteristics, such as n-grams and sentence count statistics [6], of
the statement by the subject under consideration. Use of more complex features
such as psycholinguistic features [7] based on the Linguistic Inquiry and Word
Count (LIWC) lexicon, have also been explored by [6] and shown that they are
helpful in detecting deceptive behavior. Yancheva and Rudzicz studied the relation between the syntactic complexity of text and deceptive behavior [8]. 1 In
non-verbal deception detection, physiological measures were the main source of
signals for detecting deceptive behavior. Polygraph tests measure physiological
features such as heart rate, respiration rate, skin temperature of the subject
under investigation. But these tests are not reliable and often misleading as
indicated by [9, 10] since judgment made by humans are often biased. Facial expressions and hand gestures were found to be very helpful in detecting deceptive

nature. Ekman [11] defined micro-expressions as short involuntary expressions,
which could potentially indicate deceptive behavior. Caso et al. [12] identified
particular hand gesture to be important to identify the act of deception. Cohen et al. [13] found that fewer iconic hand gestures were a sign of a deceptive
narration.
Previous research was focused on detecting deceit behavior under constrained
environments which may not be applicable in real life surroundings. Recently, the
focus has been towards experiments in real life scenarios. Towards this, PérezRosas et al. [14] introduced a new multi-modal deception dataset having real-life
videos of courtroom trials. They demonstrated the use of features from different
modalities and the importance of each modality in detecting deception. They
also evaluated the performance of humans in deception detection and compared
it with their machine learning models. Wu et al. [15] have developed methods
that leverage multi-modal features for detecting detection. Their method heavily
emphasizes on feature engineering along with manual cropping and annotating
videos for feature extraction.
In this paper, we describe our attempt to use neural models that uses features
from multiple modalities for detecting deception. We believe our work is the first
attempt at using neural networks for deceit classification. We show that with the
right features and simple models, we can detect deceptive nature in real life trial
videos more accurately.

3
3.1

Approach
Multi-Modal Feature Extraction

The first stage is to extract unimodal features from each video. We extract
textual, audio and visual features as described below.
Visual Feature Extraction For extracting visual features from the videos,
we use 3D-CNN [16]. 3D-CNN has achieved state-of-the-art results in object
classification on tridimensional data [16]. 3D-CNN not only extracts features
from each image frame, but also extracts spatiotemporal features [17] from the
whole video which helps in identifying the facial expressions such as smile, fear,
or stress.
The input to 3D-CNN is a video v of dimension (c, f, h, w), where c represents the number of channels and f, h, w are the number of frames, height,
and width of each frames respectively. A 3D convolutional filter, fl of dimension
(fm , c, fd , fh , fw ) is applied, where fm = number of feature maps, c = number
of channels, fd = number of frames (also called depth of the filter), fh = height
of the filter, and fw = width of the filter. This filter, fl , produces an output,
convout of dimension (fm , c, f − fd + 1, h − fh + 1, w − fw + 1) after sliding
across the input video, v. Max pooling is applied to convout with window size
being (mp , mp , mp ). Subsequently, this output is fed to a dense layer of size df

and softmax. The activations of this dense layer is used as the visual feature
representation of the input video, v.
In our experiments, we consider only RBG channel images, hence c = 3.
We use 32 feature maps and 3D filters of size, fd = fh = fw = 5. Hence, the
dimension of the filter, fl , is 32 × 3 × 5 × 5 × 5 . The window size, mp , of the
max pooling layer is 3. Finally, we obtain a feature vector, vf , of dimension 300
for an input video, v.
Textual Features Extraction We use Convolutional Neural Networks (CNN)
[18, 19] to extract features from the transcript of a video, v. First, we use pretrained Word2Vec [20] model to extract the vector representations for every
word in the transcript. These vectors are concatenated and fed as input vector
to the CNN. We use a simple CNN with one convolutional layer and a maxpooling layer, to get our sentence representation. In our experiments, filters of
size 3, 5 and 8 with 20 feature maps each is used. Window-size of 2 is employed
for max-pooling over these feature maps. Subsequently, a full-connected layer
with 300 neurons is used with rectified linear unit (ReLU) [21] as the activation
function. The activations of this full-connected layer is used as the textual feature
representation of the input video, v. Finally, we obtain a feature vector, tf , of
dimension 300 for an input text (transcript), t.
Audio Feature Extraction openSMILE [22] is an open-source toolkit used to
extract high dimensional features from an audio file. In this work, we use openSMILE to extract features from the input audio. Before extracting the features,
we make sure that there are no unnecessary signals in the audio that affects the
quality of the extracted features. Hence, the background noise is removed from
the audio and Z-standardization is used to perform voice normalization. To remove the background noise, we use SoX (Sound eXchange) [23] audio processing
tool. The noiseless input audio is then fed to the openSMILE tool to extract
high-dimensional features. These features are functions of low-level descriptor
(LLD) contours. Specifically, we use the IS13-ComParE openSMILE configuration to extract features which are of dimension 6373 for every input audio,
a.
After these features are extracted, a simple fully-connected neural network
is trained to reduce the dimension to 300. Finally, we obtain a feature vector,
af , of dimension 300 for an input audio, a.
Micro-Expression Features Veronica et al. manually annotated facial expressions and use binary features derived from the ground truth annotations to
predict deceptive behavior. Facial micro-expressions are also considered to play
an important role in detecting deceptive behavior. The data provided by [14]
contains 39 facial micro-expressions such as frowning, smiling, eyebrows raising,
etc. These are binary features and taken as a feature vector, mp of dimension
39.

Fig. 1. Architecture of model M LPC

3.2

Model Description

Multi-modal Model We use a simple Multi-Layer perceptron (MLP) (cite)
with hidden layer of size 1024 followed by a linear output layer. We use the rectified linear unit (ReLU) activation function [21] for non-linearity at the hidden
layer. A dropout [24] of keep probability, p = 0.5, is applied to the hidden layer
for regularization.
Figure 1 and 2 shows the architecture of our models, M LPC and M LPH+C .
Unimodal Models We perform the evaluation on individual modalities and
use the same architecture as multi-modal model. The only difference is that the
input is either vf , or af , or tf , or mf . Hence no data fusion is performed. We
name the model as M LPU .
3.3

Data Fusion

We fuse the features from individual modalities to map them into a joint space.
To achieve this, we try different kinds of data fusion techniques:
Concatenation In this method, the features from all the modalities are simply
concatenated into a single feature vector. Thus, the extracted features, tf , af , vf

Fig. 2. Architecture of model M LPH+C

and mf , are simply concatenated to form the representation: zf = [tf ; af ; vf ; mf ]
of dimension din = 939.
We call this model configuration as M LPC and is shown in the figure 1.
Hadamard + Concatenation In this method, the audio features, visual features and, textual features are fusion by using hadamard product. Then the
Micro-Expression features are concatenated with the product. Thus, we have
zf = [tf af vf ; mf ] of dimension din = 339, where (A B) is element-wise
multiplication between matrices A and B (also known as Hadamard product).
We call this model configurtion as M LPH+C and is shown in the figure 2.
3.4

Loss

The training objective is to minimise the cross-entropy between the model’s
output and the true labels. We trained the models with back-propagation using
Stochastic Gradient Descent optimizer. The loss function is:
N

J=

C

−1 XX
yi,j log2 (ŷi,j )
N i=1 j=1

(1)

Here, N is the number of samples, C is the number of categories (in our case,
C = 2). yi is the one-hot vector ground truth of ith sample and ŷi,j is its predicted
probability of belonging to class j.

4
4.1

Experiment
Data

For evaluating our deception detection model, we use a real-life deception detection dataset by [14]. This dataset contains 121 video clips of courtroom trials.
Out of these 121 videos, 61 of them are of deceptive nature while the remaining
60 are of truthful nature.
The dataset contains multiple videos from one subject. In order to avoid
bleeding of personalities between train and test set, we perform a 10-fold cross
validation with subjects instead of videos as suggested by Wu et al. [15]. This
ensures that videos of the same subjects are not in both training and test set.

4.2

Baselines

Wu et al. [15] have made use of various classifiers such as Logistic Regression
(LR), Linear SVM (L-SVM), Kernel SVM (K-SVM). They report the AUCROC values obtained by the classifiers for different combination of modalities.
We compare the AUC obtained by our models against only Linear SVM (L-SVM)
and Logistic Regression (LR).
Pérez-Rosas et al. [14] use Decision Trees (DT) and Random Forest (RF) as
their classifiers. We compare the accuracies of our models against DT and RF.

Table 1. Comparision of AUC

Features
L-SVM [15]
Random
Audio
0.7694
Visual
0.7731
Textual (Static)
0.6457
Textual (Non-static)
Micro-Expression
0.7964
All Features (Static)
0.9065
All Features (Non-static)
-

LR [15]
0.6683
0.6425
0.5643
0.8275
0.9221
-

M LPU
0.4577
0.5231
0.9596
0.8231
0.9455
0.7512
-

M LPC M LPH+C
0.4788 0.4989
0.9033 0.9348
0.9538 0.9799

5

Results

Tables 1 & 2 presents the performances of M LP and its variants along with
the state-of-the-art models. During feature extraction from text, we train the
TextCNN model with two different settings: one, by keeping the word vector
representation static; two, by optimizing the vector along with the training (nonstatic). In our results, we also show the performance of the model with these
two textual features separately. Additionally, we also mention the results we got
from our models for feature vectors initialized with random numbers.
Table 1 shows that our model, M LPH+C , obtains an AUC of 0.9799 while
outperforming all other competitive baselines with a huge margin.
Table 2. Comparing accuracies of our model with baselines

Features
Random
Audio
Visual
Textual (Static)
Textual (Non-static)
Micro-Expression
All Features (Static)
All Features (Non-static)

DT [14]
60.33%
68.59%
75.20%
-

RF [14]
50.41%
73.55%
50.41%
-

M LPU
43.90%
52.38%
93.08%
80.16%
90.24%
76.19%
-

M LPC
45.32%
90.49%
95.24%

M LPH+C
48.51%
90.99%
96.14%

Table 2 compares the performance our models with Decision Tree and Linear
Regression models [14]. Our model, M LPH+C , again outperforms other baselines
by achieving an accuracy of 96.14%. We can also infer that visual and textual
features play a major role in the performance of our models; followed by MicroExpressions and audio. This conforms with the findings by [14] that facial display
features and unigrams contribute the most to detecting deception.
As we can see that, our approach outperforms the baselines by a huge margin.
Our neural models simple and straightforward, hence the results show that right
feature extraction methods can help in unveiling significant signals that are useful
for detecting deceptive nature.

6

Drawbacks

Though our models outperform the previous state-of-the-art models, we still
acknowledge the drawbacks of our approach as follows:
– Our models still rely on a small dataset with only 121 videos. Due to this,
our models are prone to over-fitting if not carefully trained with proper
regularization.

– Also, due to the limited scenarios in the dataset, the model may not show
the same performance for out-of-domain scenarios.

7

Conclusions and Future Work

In this paper, we presented a system to detect deceptive nature from videos.
Surprisingly, our model performed well even with only 121 videos provided in
the dataset, which is generally not a feasible number of data points for neural models. As a result, we conclude that there exists a certain pattern in the
videos that provide highly important signals for such precise classification. We
performed various other evaluations not presented in this paper, to confirm the
performance of our model. From these experiments, we observed that visual and
textual features predominantly contributed to accurate predictions followed by
Micro-Expression features. Empirically, we observed that our model M LPH+C
converged faster in comparison with M LPC .
While our system performs well on the dataset by [14], we can not claim
the same performance of our model for larger datasets covering a larger number
of environments into consideration. Hence, creating a large multi-modal dataset
with a large number of subjects under various environmental condition is part
of our future work. This would pave a way to build more robust and efficient
learning systems for deception detection. Another interesting path to explore is
detecting deception under social dyadic conversational setting.

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