# Can lies be faked? Comparing low-stakes and high-stakes deception video
datasets from a Machine Learning perspective

Mateus Karvat Camara
Universidade Estadual do Oeste do Paraná
<EMAIL_ADDRESS>Adriana Postal
Universidade Estadual do Oeste do Paraná
<EMAIL_ADDRESS>Tomas Henrique Maul
University of Nottingham
<EMAIL_ADDRESS>Gustavo Paetzold
Universidade Tecnológica Federal do Paraná
<EMAIL_ADDRESS>
###### Abstract

Despite the great impact of lies in human societies and a meager 54% human
accuracy for Deception Detection (DD), Machine Learning systems that perform
automated DD are still not viable for proper application in real-life settings
due to data scarcity. Few publicly available DD datasets exist and the
creation of new datasets is hindered by the conceptual distinction between
low-stakes and high-stakes lies. Theoretically, the two kinds of lies are so
distinct that a dataset of one kind could not be used for applications for the
other kind. Even though it is easier to acquire data on low-stakes deception
since it can be simulated (faked) in controlled settings, these lies do not
hold the same significance or depth as genuine high-stakes lies, which are
much harder to obtain and hold the practical interest of automated DD systems.
To investigate whether this distinction holds true from a practical
perspective, we design several experiments comparing a high-stakes DD dataset
and a low-stakes DD dataset evaluating their results on a Deep Learning
classifier working exclusively from video data. In our experiments, a network
trained in low-stakes lies had better accuracy classifying high-stakes
deception than low-stakes, although using low-stakes lies as an augmentation
strategy for the high-stakes dataset decreased its accuracy.

##  1 Introduction

Lies are widespread throughout human societies and it is estimated that humans
lie more than twice a day [13]. In contexts such as Justice, lies can have
meaningful consequences, with 53% of the exonerations in the United States
between 1989 and 2012 involving deception [24]. Therefore, researchers have
been interested in Deception Detection (DD) for centuries [39], and, since
human accuracy for this task is merely 54% [8], attempts have been made to
build automated systems for DD, the most successful being the polygraph.
However, despite its widespread use in courts, the polygraph has proven to be
an unreliable system [17, 32] and its usage in official settings is currently
not recommended.

The quest for automated DD systems has been reinvigorated by the success of
Machine Learning (ML) applications and many researchers have directed efforts
toward this field. Even though some papers achieved impressive accuracies
above the 90% mark [58, 36, 14, 40, 20, 9, 11, 61], the proper application of
ML systems to DD in real-life is still not viable due to data scarcity [47,
39]. Few video-based DD datasets have been created and even fewer made
available for public use, hindering the development of proper techniques and
experiments in the area. But most importantly, the existing datasets lack
sufficient data for use in real-life settings, with no public DD dataset
having more than 400 videos, which is far from enough to properly train a
system capable of correctly identifying lies outside of experimental settings
[39].

Aside from the inherent challenges of building datasets based on human
subjects such as ethics and biases, DD has a conceptual barrier that
complicates matters further: the distinction between low-stakes and high-
stakes lies [59]. According to the literature [27], there is a significant
distinction between lies told in different contexts since cues for deception
will emerge in high-stakes situations in which the liars develop a stronger
emotional response, a phenomenon known as non-verbal leakage. According to
Frank and Ekman [19], “it is the presence of these emotions, such as guilt,
fear of being caught, and disgust, that can betray the liar’s deception when
they are leaked through nonverbal behaviors such as facial expressions […] or
voice tone”. In this sense, trials, interviews or negotiations could be
considered as high-stakes scenarios, while contexts that do not elicit such
strong reactions could be seen as low-stakes: truth games, roleplaying or even
situations in which participants are instructed to lie by researchers.

Even though low-stakes lies are more frequent in human lives and can be easily
simulated (faked) in controlled settings, the practical interest for automated
DD falls on high-stakes lies due to their potential impact. Based on that,
solving the issue of data scarcity for DD would mean creating a large and
diverse public dataset of high-stakes lies. However, the acquisition of this
type of data poses ethical and privacy issues and, most importantly, given
that human accuracy for DD is so low, any labeling of these samples should be
based on unassailable proof that testimonies were either truthful or
deceptive.

Ideally, ML systems would be trained on fake lies easily acquired in
controlled settings (low-stakes lies) and applied to high-stakes DD scenarios,
though the conceptual distinction between these two kinds of lies has blocked
such possibility. We then arrive at an impasse since, on one hand, high-stakes
datasets are needed for real-life applications but prove difficult to be
built, and, on the other hand, low-stakes datasets can be created more easily
from lies told in fake scenarios yet have limited practical relevance.

Despite being a possible solution to such an impasse, to the best of our
knowledge, no study has evaluated if there is, indeed, a significant
distinction between low-stakes and high-stakes deception from an ML
perspective, from which two questions of practical significance arise: “Can an
ML system trained in a low-stakes deception dataset be used to classify high-
stakes lies?” and “Can low-stakes lies be used for data augmentation in high-
stakes deception datasets?”.

Hence, this paper investigates this distinction by comparing the performance
of a Deep Learning system with two datasets: Real-life Trial (RLT) [53], a
high-stakes dataset with footage from trials and currently considered the
standard video-based DD dataset [48]; and Box of Lies (BoL) [55], a low-stakes
dataset with videos taken from a truth game on the television show “The
Tonight Show Starring Jimmy Fallon ®”. Focusing exclusively on video data from
each dataset and performing binary classification, a network based on the
Slowfast architecture [16] is created and several experiments are performed to
evaluate if the aforementioned distinction does indeed hold true based on
experimental results. We hope that, by examining this distinction, researchers
may redirect their efforts toward building large-scale deception datasets
while making the best use of the limited data available in this important
application area.

Thus, this paper is organized as follows: Sec. 2 discusses the existing public
video-based DD datasets and ML papers that use them, Sec. 3 describes our
methodology, Sec. 4 presents and discusses the results from our experiments
and Sec. 5 summarizes our findings.

##  2 Deception detection datasets

Back in 2012, Gokhman _et al_. [21] and Fitzpatrick and Bachenko [18]
discussed the development of a standard Deception Detection (DD) dataset,
since none was available at the time, which required every paper to build its
own dataset, limiting comparisons between studies and hindering the
advancement of the field. Since then, many papers have been published with
experiments conducted in video-based datasets which were not publicly
available [51, 56, 6, 39, 7] due to the sensitive nature of the task. However,
four video-based public datasets (presented in Tab. 1) have been created and
are discussed here.

We focus on datasets with videos for their simplicity of being deployed in
real-life situations, in contrast to Electroencephalogram (EEG) or Functional
near-infrared spectroscopy (fNIRS) which require special equipment to acquire
data, and for the fact that they present the best results, in relation to
audio and text, in papers that compare the performance of unimodal
classification [40, 48, 61]. Yet, several papers have worked with DD datasets
from other modalities (some of which are publicly available), such as EEG [3,
1, 15], fNIRS [30], audio [62, 49, 22] and text [12, 29, 10, 31].

Dataset | Category | Videos | Individuals | Observations
---|---|---|---|---
Real-life Trial [53] | High-stakes | 121 (110) | 56 (51) |  | Standard dataset for
---
Deception Detection [48]
Box of Lies [55] | Low-stakes | 68 (93) | 26 (33) | -
| Miami University Deception
---
Detection Dataset [46]
Low-stakes | 320 | 80 | Balanced dataset
Bag-of-Lies [26] | Low-stakes | 325 | 35 |  | Includes EEG and
---
Gaze information
Table 1: Publicly available video datasets for Deception Detection. Numbers in
parentheses are presented for reference and correspond to the numbers used in
this paper for the respective datasets.

###  2.1 Low-stakes datasets

To the best of our knowledge, there are currently three publicly available
video-based low-stakes DD datasets: Bag-of-Lies [26], Miami University
Deception Detection Dataset (MU3D) [46] and Box of Lies (BoL) [55].

Bag-of-Lies [26] is described by its authors as a “casual deception” dataset,
containing 325 videos of 35 volunteers that had to describe images from a
selected set being free to be truthful or deceptive. Being a multimodal
dataset, it has visual, audio and gaze data (acquired with an eye tracker) for
all testimonies, and EEG data for 201 videos.

The MU3D [46] was created with the goal of being a standardized, unbiased and
balanced dataset, being made of 320 videos from 80 subjects (20 Black female,
20 Black male, 20 White female, and 20 White male), each one having 2 lies and
2 truths. In each video, participants describe people they like and people
they dislike, with all videos having audio and accompanying transcriptions.

The BoL dataset [55] features videos from participants playing the “Box of
Lies” game in the “The Tonight Show Starring Jimmy Fallon ®” television show.
Played between a celebrity guest and the show’s host, the game consists of
multiple rounds in which players describe (deceptively or truthfully) an
object which is hidden from the other player, with the listener having to
guess whether the description was a truth or a lie. Sample frames are
presented in Fig. 1.

Figure 1: Sample frames from the Box of Lies dataset (BoL) [55] which contains
videos of low-stakes deception from a television show. The top two frames are
lies and the bottom two are truths.

The BoL dataset provides transcriptions, annotations on gestures and facial
displays, as well as labels for small segments (called utterances) within each
round played, with the videos being available on YouTube in an official
playlist of the television show. When the Box of Lies paper [55] was
published, 68 rounds were available, though currently this number has gone up
to 93 rounds. Using these 68 rounds, the original Box of Lies paper achieves
an accuracy of 65% with a Random Forest classifier on multimodal data, while
Zhang _et al_. [64], also using Random Forest, achieves 73% accuracy for
multimodal classification and 67% for classification solely on videos.

###  2.2 High-stakes datasets

To the best of our knowledge, the only publicly available video-based high-
stakes DD dataset is the Real-life Trial dataset [53] (RLT), which makes it
the standard dataset for papers that apply ML techniques to DD [48]. It
originally consists of 121 videos from 56 unique individuals taken from trials
in which truthful or deceptive testimonies were verified by the police as
such, thus allowing the dataset creators to objectively label each video. It
is available on one of its authors’ page [50] and accompanies transcriptions
for each video and annotations on non-verbal behavior such as facial displays
and hand gestures. Sample frames from the dataset are presented in Fig. 2.

Figure 2: Sample frames from the Real-life Trial dataset (RLT) [53] which
contains videos of high-stakes deception from trials. The top two frames are
lies and the bottom two are truths.

Several papers have used the RLT dataset for ML experiments, which have their
results and techniques summarized in Tab. 2. Among these, data augmentation
strategies, classifiers, feature selection and preprocessing techniques vary
greatly. With a single exception [11], all papers that combine video, audio
and transcripts achieve better results in multimodal classification than in
unimodal classification from videos, showcasing the strength of combining
multiple types of data for this task. But most importantly, all papers achieve
results above human accuracy for DD (54% [8]) and the accuracy measured for
this specific set of videos by the creators of RLT in a set of experiments
with human volunteers (last row of Tab. 2).

Paper | Classifier | Details |  | Acc.
---
V
| Acc.
---
V+A+T
[58] | CNN + LSTM | Vague methodology | 100 | -
[36] | CNN | Preprocessing with Local Binary Patterns | 97.35 | 97.33
[14] | CNN | ResNet [28], GANs [23] | 93.61 | 97.00
[40] | 3DCNN | Fusion by Hadamard product | 93.08 | 96.14
[20] | 3DCNN | Fusion by concatenation | 78.57 | 96.42
[52] | CNN + RNN | Facial reconstruction | 72.8 | -
[9] | SVM | Features from AlexNet [41], Multiview learning | 99 | 99
[11] | kNN | Manually annotated behavioral cues | 94 | 78
[61] | Logistic Regression | Features: Improved Dense Trajectory (IDT) [60] | 89.88* | 92.21*
[66] | NN | Features: Facial displays and hand gestures | 78.53 | 84.18
[2] | RBF-SVM | Features: Action Units (AUs) [4] | 76.84 | -
[48] | SVM | Features from OpenFace [5], Affect [54] | 76 | 84
[35] | LMNN | Features from CNN + LSTM network | 75 | 84.16
[63] | RF, SVM | Emotion classification | 71.15 | 87.59
[53] | DT | Features: Facial displays and hand gestures | 68.59 | 75.20
[33] | SVM | Features: AUs [4] | 67.2 | 78.95
[47] | Linear SVM | Features: IDT [60], Gender classifier | 64.6 | -
[47] | Linear SVM | Features: IDT [60] | 57.4 | -
[53] | Human performance | Average between 3 annotators | 46.50 | 56.47
Table 2: Comparison of papers that use the Real-life Trial dataset [53] for
Deception Detection with Machine Learning. Accuracy for classification
exclusively from videos (Acc. V) is presented, as well as multimodal
classification with Video, Audio and Transcripts (Acc. V+A+T). Marked
accuracies (*) are actually AUC. Papers that use exclusively Deep Learning
classifiers are grouped on top for convenience. All classifiers achieve better
results than the measured human performance for this dataset presented on the
bottom row.

However, according to Belavadi _et al_. [6], “there is insufficient evidence
that AI systems for detecting deception are likely to achieve adequate
accuracy in real-world use” and, given that the RLT dataset is small, such
impressive experimental results do not provide such evidence. Moreover,
Mambreyan _et al_. [47] have shown that the RLT dataset has significant gender
bias which can be exploited by an ML classifier, further highlighting the need
for bigger, more diverse and less biased publicly available video-based high-
stakes DD datasets.

##  3 Methodology

To evaluate a possible distinction between low-stakes and high-stakes
deception from a Machine Learning (ML) perspective, the Box of Lies (BoL) [55]
and Real-life Trial (RLT) [53] datasets were selected. While RLT is, to the
best of our knowledge, the only publicly available video-based high-stakes
Deception Detection (DD) dataset, BoL is the low-stakes dataset that has the
closest number of videos to RLT (considering its current number of 93 rounds),
making it a fairer comparison than other low-stakes datasets which have almost
thrice the number of videos of RLT.

###  3.1 Dataset preparation

Following the steps taken by other papers that work with RLT [52, 14, 61, 48,
33, 9], we remove videos from the dataset which are deemed unsuitable for
classification based solely on visual data, such as those in which the
speaker’s face is hidden or out of focus during most of the video, or where
there are multiple people in the foreground. After that, the dataset was
reduced to 110 videos portraying 51 individuals. Among the remaining videos,
some had noisy frames or frames showing other people instead of the speaker,
so they were edited with Kdenlive [38] and these frames were removed. The full
list of removed and edited videos is available in the Supplementary Material.

While the BoL dataset was originally labeled by utterance, the RLT dataset is
labeled by video, which required a change in labels in BoL. Therefore, each
round was taken as a single video labeled according to the veracity of the
object’s description and, for each video, the frames not showing the speaker
were removed with Kdenlive [38]. Despite assigning a single label to whole
videos (which contain truthful and deceptive segments), such labeling scheme
follows the scheme from RLT (allowing for better comparison between datasets)
and removes subjectivity from the process. By contrast, the original labeling
by utterance categorized segments with unclear veracity as deceptive, which
resulted in 82.2% of the samples being labeled as deceptive.

After this dataset preparation step, the resulting BoL and RLT datasets were
such as presented in Tab. 3, from which it can be inferred that an ML system
trained in RLT should have better results than a similar one trained in BoL,
since RLT has more data while also being more diverse (more individuals) and
balanced. To illustrate such balance, the individual with the highest number
of videos in RLT is depicted in 21 videos (19.1% of the videos) while for BoL,
this number goes up to 34 videos (36.6% of the videos).

| Real-life Trial | Box of Lies
---|---|---
Videos | 110 | 93
Individuals | 51 | 33
| Average number of
---
videos by individual
2.2 | 2.8
| Standard deviation of
---
videos by individual
3.5 | 5.6
| Average
---
video length
26.9 s | 20.8 s
Table 3: Comparison between the Real-life Trial (RLT) [53] and Box of Lies
(BoL) [55] datasets after dataset preparation. Each video portrays a single
individual. Not only does RLT have more videos depicting a greater number of
individuals with a longer average length, but it also is more balanced in
regards to the number of videos by individual (a higher standard deviation of
videos by individual indicating a less balanced dataset).

Also, considering the findings from Mambreyan _et al_. [47], we consider
biases present in each dataset, which are shown in Tab. 4. Despite not being a
desirable property of the dataset, RLT has a significant gender bias, which
should also make ML systems trained on it have better results due to bias
exploitation.

| Real-life Trial | Box of Lies
---|---|---
Truth ratio | 51.8% | 46.3%
Women ratio | 61.8% | 48.4%
| Truth ratio
---
for women
35.3% | 53.3%
| Truth ratio
---
for men
78.6% | 39.6%
Table 4: A comparison of biases in the Real-life Trial (RLT) [53] and Box of
Lies (BoL) [55] datasets after dataset preparation. Even though BoL has a
slight bias toward lies, RLT has a significant gender bias.

As a final preparation step, both datasets are enriched with copies of their
videos flipped horizontally, doubling the number of videos in each dataset.

###  3.2 Network training

Focusing solely on DD from videos (as discussed in Sec. 2), we use the
Slowfast [16] architecture to perform binary classification. This architecture
was chosen for its balance between classification accuracy and low
computational cost, achieving better results than other Deep Learning video
recognition architectures that have similar computational cost, such as Hidden
TSN [65], TSM [44], TEINet [45], MSNet [42], TEA [43], STM [34] and CSN [57].

Such criterion was used due to limited computational power and time allocation
of resources for our experiments, which were performed in a shared computer
with an Intel Core i3-10100F 3.6 GHz CPU, 16 GB 2666 MHz RAM and a GTX 1650 4
GB GPU. These limitations also prompted us to use the Slowfast implementation
available in the GluonCV framework [25] for its optimizations and ease of
implementation, as well as performing non-exhaustive hyperparameter search
prior to k-fold testing.

For each network trained, hyperparameter search was performed independently,
with the best 5 hyperparameter combinations later being used with 5-fold
testing. Since non-exhaustive search was performed, the 5 best combinations
were used for testing as a means of overcoming a possible deficit from the
non-exhaustive search. An 80/20 split was performed for hyperparameter search,
with an initial set of values being tested, and the following combinations
being chosen according to the previous combinations’ accuracy on the
validation set. Therefore, hyperparameter values that did not perform well
were assessed but soon discarded. Such strategy meant not all hyperparameter
combinations were evaluated, but results on the validation set were considered
satisfactory given that each combination had its results thoroughly analyzed.

Hyperparameters evaluated and their corresponding values were:

  * •

Slowfast configuration: 4x16 and 8x8;

  * •

Optimizer: SGD, Adam and RMSProp;

  * •

Learning rate: ranging from $10^{-1}$ to $10^{-6}$ in exponential increments;

  * •

Weight decay: $10^{-2}$, $10^{-4}$, $10^{-6}$ and no weight decay;

  * •

Learning rate decay strategy: reducing learning rate by a factor of 10 every
40 epochs, every 10 epochs, or not reducing it at all;

  * •

Momentum: 0.98, 0.9 and 0.5.

For most trials, 100 epochs were used, but for combinations that had not
converged by 100 epochs, greater numbers were also evaluated. The batch size
was limited to 1 since there was not enough memory available for greater
values, a limitation which also inhibited the use of different Slowfast
backbones apart from ResNet50 [28]. The list of all hyperparameter
combinations evaluated is available in the code repository for this paper.

###  3.3 Experimental setup

Our experimental setup is presented in Fig. 3. As a first step, hyperparameter
search is performed considering each dataset independently. The 5
hyperparameter combinations with the highest validation accuracies are then
used for 5-fold testing with their respective datasets. Even though both use
the Slowfast architecture, the combination that yields the best accuracy for
RLT is named Net 1 while the one which yields the best accuracy for BoL is
named Net 2. Both are considered optimized for their respective datasets.

Figure 3: Experimental setup used to evaluate differences between a low-stakes
deception dataset (Box of Lies - BoL [55]) and a high-stakes one (Real-life
Trial - RLT [53]). The network used was Slowfast [16], with three distinct
training configurations based on the hyperparameter searches performed for the
distinct training sets (RLT, BoL and RLT+BoL). Letter tags are used in each
Experiment for later reference.

Trained on the RLT dataset, Net 1 is then used for inference on the BoL
dataset while Net 2, having been trained on BoL, is used for inference on RLT
(Step 2 in Fig. 3). Since both Net 1 and Net 2 were optimized for their
datasets, this step allows us to evaluate whether each kind of deception
dataset (high-stakes and low-stakes) can be used as training data for
inference on the other kind.

Finally, both datasets are combined (Step 3 in Fig. 3) and hyperparameter
search is performed for the mixed dataset, to evaluate the efficacy of
combining the two kinds of deception datasets. The 5 best hyperparameter
combinations are used for 5-fold testing and the best one is named Net 3.

Aiming to evaluate possible data augmentation strategies, both Nets 1 and 2
are retrained (using their previous set of hyperparameters) on the mixed
dataset and tested with their original datasets.

##  4 Results and discussion

Given that 5-fold testing was performed for all experiments except cross-
testing, a total of 27 runs were conducted for testing. Hyperparameter search
for Nets 1, 2 and 3 required 44, 176 and 77 runs, respectively. Therefore, a
total of 324 runs were performed. If an exhaustive hyperparameter search was
performed along with 5-fold (exhaustive search being done for each fold), the
number of runs would be above 10000, which would not be viable given our
previously described computing power limitations. Even though our approach
does not guarantee optimal results, it achieves adequate accuracies with a
fraction of the resources needed. The results from hyperparameter search runs
are available on the code repository of this paper.

###  4.1 Network optimization

The hyperparameter search for Nets 1, 2 and 3 was performed and the 5 best
combinations of each were later used for 5-fold testing. The combination which
had the best result was considered the one that better optimized its
respective network. Tab. 5 presents the hyperparameter values for each of
these combinations.

Network |  | Slowfast
---
Configuration
| Learning
---
Rate
| Learning Rate
---
Decay Strategy
Optimizer | Momentum |  | Weight
---
Decay
Epochs
Net 1 | 4x16 | $5*10^{-4}$ | Every 40 epochs | SGD | 0.9 | $10^{-4}$ | 100
Net 2 | 8x8 | $10^{-3}$ | Every 10 epochs | SGD | 0.5 | $10^{-6}$ | 100
Net 3 | 4x16 | $10^{-5}$ | No | Adam | - | $10^{-4}$ | 200
Table 5: Best hyperparameter combinations for each of the networks trained
after hyperparameter search and 5-fold testing were performed. Despite all
networks using the same architecture and performing the same task, differences
in training data caused significant differences in the hyperparameter
combinations for each network.

The results of the optimization step for each of the networks are presented in
Tab. 6, from which the assumptions raised in Sec. 3.1 hold true: the RLT
dataset (Exp. A) had better results than BoL (Exp. B). Both networks had
results above human accuracy for DD (54%), however both were below the results
achieved by most works presented in Sec. 2, which can be attributed to
experimental limitations (such as a batch size of 1) and the fact that the
Slowfast architecture was originally created for the Kinetics-400 [37]
dataset, which has considerably more data with 306245 videos, and often showed
overfitting in our experiments.

Net. | Exp. | Training | Testing | Acc.
---|---|---|---|---
Net 1 | A | RLT | RLT | 68.64%
Net 2 | B | BoL | BoL | 55.92%
Net 3 | E | RLT+BoL | RLT+BoL | 59.12%
Table 6: Comparison of results (Acc.) obtained in experiments (Exp.) for each
of the networks (Net.). Hyperparameter search was conducted for each distinct
training set to optimize results. While RLT obtained the best results, it is
clear that BoL is a difficult dataset and it reduces overall accuracy if
combined with RLT.

The combination of both datasets (Exp. E), however, had its results closer to
those from BoL (Exp. B) than those from RLT (Exp. A), indicating that an
increase in training data, by itself, is not enough to achieve better results.
It is unclear to us whether these results occurred due to an improper
combination of data with a significant semantic difference (low-stakes lies
and high-stakes lies) or because of properties of these specific datasets
(gender bias, category bias, imbalance of the number of videos by individual).

###  4.2 Real-life Trial (Net 1) results

The results from experiments done with Net 1 are presented in Tab. 7, which
highlights the previous considerations on the difficulty of each dataset.
While the best results are seen when RLT is trained and tested on its own
(Exp. A), cross-testing on BoL (Exp. C) had results below human accuracy, and
adding BoL videos as an augmentation strategy (Exp. F) ends up reducing the
original accuracy (Exp. A).

Exp. | Training | Testing | Accuracy
---|---|---|---
A | RLT | RLT | 68.64%
C | RLT | BoL | 44.09%
F | RLT+BoL | RLT | 56.82%
Table 7: Comparison of results obtained for experiments (Exp.) on Net 1.
Cross-testing had significantly lower results, indicating the difficulty of
the BoL dataset. Adding BoL samples to the training data lowered accuracy.

Even though this cross-testing scenario does not hold much practical purpose
since pragmatic interest lies in testing being performed with high-stakes lies
and training with low-stakes lies, the evaluation of the augmentation strategy
meets such interests. That being so, in our experiments, using low-stakes lies
to augment a high-stakes deception dataset resulted in worse performance than
working with the high-stakes dataset on its own. The results suggest that this
behavior occurred due to semantic differences between these datasets (high-
stakes and low-stakes deception). However, it is important to point out that
the reduction in performance might have been caused by the aforementioned
properties within the datasets which are unrelated to these semantic
differences.

###  4.3 Box of Lies (Net 2) results

Experiments performed with Net 2 are shown in Tab. 8, which presents
noteworthy results. Even though training and testing in BoL reached a low
accuracy (Exp. B), cross-testing with RLT (Exp. D) increased this accuracy and
using RLT to augment data on BoL (Exp. G) significantly improved results.

Exp. | Training | Testing | Accuracy
---|---|---|---
B | BoL | BoL | 55.92%
D | BoL | RLT | 58.64%
G | RLT+BoL | BoL | 62.87%
Table 8: Comparison of results obtained for experiments (Exp.) on Net 2.
Cross-testing improved results, indicating that RLT is an easier dataset than
BoL. Adding RLT samples to the training data increased accuracy further.

In view of the considerations previously presented on the difficulty of each
dataset, the results from Exp. G can be understood from the perspective that
videos from RLT make training easier, allowing the network to better identify
patterns within the testing dataset (BoL). From another point of view, it
might be argued that high-stakes deception are more easily identified than
low-stakes, making them suitable for data augmentation.

However, Exp. D’s results are not entirely conclusive. It might be argued
that, since training was performed in a difficult dataset, testing in an
easier one would improve results. Despite that, such an experimental scenario
perhaps holds the greatest practical interest due to the ease of acquiring
low-stakes deception data and the difficulty of doing so for high-stakes data
(which gets the most practical interest for DD systems). From our experiments
alone, it could be said that an ML system trained with low-stakes lies could
be acceptably applied to a set of high-stakes lies. However, due to our
experimental limitations and limited data, such findings cannot be generalized
for all DD datasets.

##  5 Conclusion

We set out to tackle the issue of data scarcity in Deception Detection (DD),
aiming to shed light on the conceptual distinction between high-stakes and
low-stakes lies by presenting numerical data that might support researchers on
the task of building new datasets for such an important application area.
Currently, datasets for DD are either built with high-stakes or low-stakes
lies and applications trained in one kind theoretically can only be used for
inference on lies of that same kind. Through our investigation, we evaluated
this distinction by experimentally comparing two datasets: Real-life Trial
(RLT) [53], a high-stakes DD dataset, and Box of Lies (BoL) [55], a low-stakes
DD dataset. Different experiments were performed with the Slowfast [16]
architecture and their results were used to analyze whether such a distinction
holds true. From these experiments we found that:

  1. 1.

The network trained in high-stakes lies performed better than the network
trained in low-stakes lies;

  2. 2.

Combining both datasets into a single one had worse results than working with
the high-stakes dataset on its own;

  3. 3.

Using the network trained in high-stakes deception for inference on low-stakes
deception had results below human accuracy;

  4. 4.

Using low-stakes deception as a data augmentation strategy for the high-stakes
dataset did not improve results;

  5. 5.

The network trained in low-stakes lies had better accuracy classifying high-
stakes deception than low-stakes;

  6. 6.

Using high-stakes lies as a data augmentation strategy for the low-stakes
dataset showed a significant improvement in results.

Given these findings, we conclude that there is a clear distinction between
the RLT and BoL datasets. However, similar experiments should be performed
with different datasets to assess whether these results stem from differences
in these datasets’ incidental properties (mainly bias and amount of data) or
from a deep semantic difference in their data (low-stakes and high-stakes
lies). Nonetheless, to the best of our knowledge, we have shown, for the first
time, that a low-stakes DD dataset can be acceptably used to train a Machine
Learning (ML) classifier created for inference on high-stakes deception data.

Finally, we highlight the need for bigger, less biased and more balanced
publicly available video-based DD datasets. Despite impressive results in
experimental settings, ML DD systems are not yet ready for deployment in real-
life settings given the lack of sufficient data for their proper training.

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# Quantum Teleportation with One Classical Bit

Abhishek Parakh  University of Nebraska, Omaha, NE 68116

###### Abstract

Quantum teleportation allows one to transmit an arbitrary qubit from point A
to point B using a pair of (pre-shared) entangled qubits and classical bits of
information. The conventional protocol for teleportation uses two bits of
classical information and assumes that the sender has access to only one copy
of the arbitrary qubit to the sent. Here, we ask whether we can do better than
two bits of classical information if the sender has access to multiple copies
of the qubit to be teleported. We place no restrictions on the qubit states.
Consequently, we propose a modified quantum teleportation protocol that allows
Alice to reset the state of the entangled pair to its initial state using only
local operations. As a result, the proposed teleportation protocol requires
the transmission of only one classical bit with a probability greater than
one-half. This has implications for efficient quantum communications and
security of quantum cryptographic protocols based on quantum entanglement.

###### keywords:

quantum teleportation, one-classical-bit

## Introduction

Quantum teleportation [1, 2] is a strikingly curious quantum phenomenon with
myriads of applications ranging from secure quantum communication to
distributed quantum computing [3, 4, 5, 6, 7]. First presented by Bennett et
al. [1], quantum teleportation allows one to recreate an arbitrary qubit from
just two bits of classical information. The precondition is that the two
communicating parties share an entangled pair of qubits. Entanglement,
essentially, transmits the raw values of amplitudes present in the arbitrary
qubit and the classical bits provide a final “correction” to these values.
Quantum teleportation has proven to be an invaluable tool in quantum
information science [8, 9, 10]. Quantum teleportation enables the development
of quantum repeaters [3, 11], a pivotal technology for the establishment of
quantum communication networks [12], and in general the framework of quantum
Internet. Quantum teleportation has been implemented in lab setting using a
number of different resources [13, 14]. Lastly, a number of cryptographic
protocols use shared entangled qubits and operations equivalent to
teleportation to produce random numbers and encryption keys [15].

It is known that, given a pre-shared Bell pair, the teleportation of an
unknown qubit, requires the transmission of two classical bits [1]. More
generally, the teleportation of an $N$-state requires 2$log_{2}N$ classical
bits [16]. This, however, assumes that the sender has access to only one copy
of the arbitrary unknown qubit that she is teleporting. Kak investigated the
minimum number of classical bits required for teleportation [17]. In turn, he
proposed three variations on Bennett’s protocol that required fewer than two
qubits. Two of these variations, however, use a non-traditional setup where
the entangled qubits are not pre-shared between the communicating parties but
transmitted along with the classical bits of information. This change in the
setup can be looked upon as an encryption system where the classical bit of
information acts as the encryption key for the qubit and multipath routing can
be used for their transmission. From the viewpoint of teleportation,
nevertheless, if the qubits are to be transmitted along with classical bits,
then one could transmit the arbitrary qubit directly at that time without the
need to resorting to teleportation. However, it does point out to the
possibility of some “non-standard” setting of quantum teleportation having
lower classical cost of communication. One such “non-standard” setting was
investigated by Pati [18] where the teleported qubit arose from a specific
portion of the Bloch sphere leading to a one-bit classical communication cost.
This was further investigated by Lo as remote state preparation [19].

In this paper, we too consider a somewhat non-standard setting but in terms of
resources. Like the traditional teleportation protocol, we assume that the
communicating parties pre-share entangled qubits. However, we consider the
case when the sender has more than one copy of an (unknown) arbitrary qubit
$\ket{\phi}=a\ket{0}+b\ket{1}$, $|a|^{2}+|b|^{2}=1$. We do not place any other
restrictions on the qubits. Such a possibility has not been investigated
before. Our result shows that under such as setting, we can do better than two
classical bits of communication. A sender may have multiple copies of the
qubit to be teleported in many practical situations where the unknown qubit is
either the result of a periodic or on-demand natural process or output of a
quantum algorithm. One may need to send this (unmeasured) output to the
receiver for further processing. We show that the teleportation of such a
qubit requires the transmission of only one classical bit with a probability
greater than one-half. In order to do so, we propose a reset procedure that
allows for the recreation of the original shared Bell state using only local
operations at the sender’s end and no consumption of resources at receiver’s
end. In the proposed reset procedure, as with conventional teleportation
protocol, we begin with maximally entangled qubits. At the first stage of the
protocol, if this maximally entangled qubit enters into a desired state we
move on to the second stage of the protocol. If, on the other hand, the
entangled part ends up in an undesired state we reset it back to the original
state using only local operations at the sender’s end. The cost of doing so is
one copy of the arbitrary qubit, $\ket{\phi}$.

Furthermore, if the amplitudes $a$ and $b$ are known and/or the qubit,
$\ket{\phi}$, generated on demand, then the reset operation may be attempted
many times over.

The result presented in this paper, we believe, represents a fundamental
advance in the classical burden of teleportation, since it was proposed by
Bennett et al.[1], because we do not restrict the states of the qubit and
allow the parties to pre-share entangled pairs as in the traditional setting.
The presented result also has widespread consequences for quantum
cryptographic protocols that use entangled pairs and assume a symmetric power
between communicating parties.

##  1 Result

It is useful to think of the conventional teleportation protocol as a two
stage system [19]. We start with a shared Bell pair
$\frac{\ket{00}+\ket{11}}{\sqrt{2}}$ between sender, Alice, and receiver, Bob.
The first stage of the teleportation protocol introduces the amplitudes $a$
and $b$ into the entangled pair. However, the locations of the amplitudes
maybe flipped and is corrected with the application of Pauli X gate by both
the sender and the receiver. This requires the transmission of 1-bit of
classical information to Bob. In the second stage of the protocol, Alice
terminates the entanglement by measuring her qubit of the Bell pair in the
Hadamard basis. This introduces another undesirable error into the state of
the qubit that Bob holds corresponding to the Pauli Z gate. This is corrected
by the transmission of another classical bit of information to Bob.

To be more precise, in Bennett’s protocol [1] at the end of the first stage
the two parties share the state $a\ket{00}+b\ket{11}$ or
$b\ket{00}+a\ket{11}$. The first of these states is desirable and the second
one is converted to the first one with the application of a bi-local unitary
transformation $\ket{0}\rightarrow\ket{1}$. This requires one bit to be sent
to Bob. In the second stage of the protocol, Bob ends up with
$a\ket{0}+b\ket{1}$ or $a\ket{0}-b\ket{1}$. The correction of the last state
requires the other bit of information to be sent to Bob. In the end Bob is
left with $a\ket{0}+b\ket{1}$, which corresponds to the unknown state
$\ket{\phi}$ that Alice wanted to teleport.

In this paper, we ask the simple question: if Alice ends up in the undesired
state at the end of the first stage, i.e. $b\ket{00}+a\ket{11}$, can she using
only local transformations, reset the state of the shared entanglement to the
original state $\frac{\ket{00}+\ket{11}}{\sqrt{2}}$? If so, she can reattempt
the teleportation protocol.

###  1.1 Resetting the Entangled State

In order to reset the entangled state, Alice introduces an ancillary qubit
$\ket{\theta}=c\ket{0}+d\ket{1}$ into the system. It will be come apparent
through the following discussion that $\ket{\theta}=\ket{\phi}$. The resulting
system state is given by $\ket{\psi}$,

$\begin{split}\ket{\psi}&=\ket{\theta}(a\ket{1_{A}1_{B}}+b\ket{0_{A}0_{B}})\\\
&=(c\ket{0_{l}}+d\ket{1_{l}})(a\ket{1_{A}1_{B}}+b\ket{0_{A}0_{B}})\\\
&=ca\ket{0_{l}1_{A}1_{B}}+cb\ket{0_{l}0_{A}0_{B}}+da\ket{1_{l}1_{A}1_{B}}+db\ket{1_{l}0_{A}0_{B}}\end{split}$
(1)

Qubits with subscripts $l$ and $A$ are with Alice and the qubit denoted by
subscript $B$ is with Bob. Now, Alice applies CNOT with the ancillary qubit
(the first qubit) as the control qubit and second qubit as the target qubit.
The state then becomes,

$\begin{split}\ket{\psi}&=ca\ket{0_{l}1_{A}1_{B}}+cb\ket{0_{l}0_{A}0_{B}}+da\ket{1_{l}0_{A}1_{B}}+db\ket{1_{l}1_{A}0_{B}}\\\
&=\ket{1_{A}}(ca\ket{0_{l}1_{B}}+db\ket{1_{l}0_{B}})+\ket{0_{A}}(cb\ket{0_{l}0_{B}}+da\ket{1_{l}1_{B}})\end{split}$
(2)

In Equation 2 above, we have removed the second qubit as the common qubit for
simplification of the expression. We see that
$\ket{0_{A}}(cb\ket{0_{l}0_{B}}+da\ket{1_{l}1_{B}})$ is closest to the desired
state $\frac{\ket{00}+\ket{11}}{\sqrt{2}}$. If we set $c=a$ and $d=b$ then we
get,

$\ket{\psi}=\ket{1_{A}}(aa\ket{0_{l}1_{B}}+bb\ket{1_{l}0_{B}})+\ket{0_{A}}(ab\ket{0_{l}0_{B}}+ba\ket{1_{l}1_{B}})$
(3)

Alice now measures the second qubit.

She will see $\ket{0}$ with probability $2|ab|^{2}$ and the shared entangled
qubits end up in state $\frac{\ket{00}+\ket{11}}{\sqrt{2}}$. This constitutes
a successful reset by Alice. Note that, setting $c=a$ and $d=b$ means that
Alice is simply using a copy of the original qubit $\ket{\phi}$ for the reset
operation and does not need to create any new special ancillary qubits. Once a
reset to the original Bell state is achieved, Alice reinitiates the
teleportation protocol using a fresh copy of $\ket{\phi}$.

On the other hand, Alice’s measurement results in $\ket{1}$ with probability
$|a^{2}|^{2}+|b^{2}|^{2}$. This takes the system to state
$\frac{a^{2}\ket{01}+b^{2}\ket{10}}{\sqrt{|a^{2}|^{2}+|b^{2}|^{2}}}$. Alice
can apply the $X$ gate to her qubit and reattempt the reset operation with
$\frac{a^{2}}{\sqrt{|a^{2}|^{2}+|b^{2}|^{2}}}$ and
$\frac{b^{2}}{\sqrt{|a^{2}|^{2}+|b^{2}|^{2}}}$ as the new $a$ and $b$ values,
respectively.

At this point, Alice has consumed two copies of $\ket{\phi}$. One during stage
one of the teleportation protocol and second as the ancillary qubit introduced
into the system during the reset operation. Figure 1 shows the branches that a
teleportation attempt with the proposed reset procedure can follow.

Figure 1: Decision tree at Alice’s end for teleportation with the proposed
reset operation. Probabilities are shown along the arms between the nodes.
States in the red boxes reside with Bob.

##  2 Discussion

The probability of success of the reset attempts vary with the values of
amplitudes $a$ and $b$, in the state $\ket{\phi}$, and is given by $2|ab|^{2}$
for the first reset attempt. Since,
$|ab|^{2}=|a|^{2}|b|^{2}=|a|^{2}(1-|a|^{2})=|a|^{2}-|a|^{4}$ and
$0\leq|a|^{2}\leq 1$, we get $|ab|^{2}\leq\frac{1}{4}$ and
$2|ab|^{2}\leq\frac{1}{2}$.

For example, when $a=\frac{i}{\sqrt{2}}$ and $b=\frac{1}{2}+\frac{i}{2}$, the
probability that the first reset attempt will succeed is $\frac{1}{2}$. After
a successful reset, Alice makes a second attempt to teleport the qubit. The
probability that entangled qubits will collapse into the desirable state
($a\ket{00}+b\ket{11}$) is again $\frac{1}{2}$. Therefore, with probability
$\frac{1}{2}+\frac{1}{4}$ Alice would transmit just one bit for teleportation
but must have at least three copies of $\ket{\phi}$ at her disposal.

Remarkably, the qubit $\ket{\phi}$ remains unknown and arbitrary.

Consequently, in general, considering only the first reset attempt and an
unknown qubit $\ket{\phi}$ the probability of ending up in the desired state
($a\ket{00}+b\ket{11}$), that is a successful teleportation with only one-
classical bit, is $\frac{1}{2}+\frac{1}{2}\cdot
2|ab|^{2}=\frac{1}{2}+|ab|^{2}$. Note, this probability is always set to
$\frac{1}{2}$ in Bennett et al.’s quantum teleportation [1] protocol which
always requires two bits of information to be transmitted.

Alice, however, does require the values of $a$ and $b$ for the reset attempts
beyond the first one; in the case of a reset failure. This latter case is
useful when the precision required for classical representation of the $a$ and
$b$ values exceeds the available bandwidth or resources available on the
classical channel. In other words, if the values of $a$ and $b$ are known,
Alice can pursue the reset attempts until it succeeds. Note that once a reset
attempt succeeds, the probability that we will end up in the desired entangled
state is again $\frac{1}{2}$. Therefore, the expected number of attempts,
given a successful reset, is 2.

The reset procedure presented above, when successful, reduces the
teleportation protocol to one stage protocol under the standard setting of
pre-shared Bell states. Furthermore, Alice knows at each stage whether the
reset has succeeded or not. In the worst case, if the values of $a$ and $b$
are not known, and the reset fails, she can abandon that particular pair of
entangled qubits and use another. The computational burden of teleportation
with reset can then be stated as,

$H(T)=\left[(\frac{1}{2}+|ab|^{2})(1)+(\frac{1}{2}-|ab|^{2})(2)\right]\mbox{bits}$
(4)

It is easy to see that $1.25\leq H(T)\leq 1.5$ depending on the values of $a$
and $b$. For comparison, one of the protocols by Kak [17] reduces the
computational burden to 1.5 bits when the Bell states are pre-shared. The cost
of reducing the computational burden to 1.25 bits, therefore, is the
expenditure of extra copies of the unknown qubit $\ket{\phi}$. In other words,
the probability $\frac{1}{2}+|ab|^{2}$ only represents the average case. Given
that the proposed protocol is probabilistic in nature and if Alice has several
qubits to teleport, she may end up with a situation where all her reset
attempts may succeed resulting in a 1 bit per qubit for teleportation even for
a completely arbitrary unknown qubit!

One may argue that even in Bennett’s original teleportation protocol [1],
Alice and Bob may agree on a scheme such as the follows: if stage one of the
protocol succeeds in creating $a\ket{00}+b\ket{11}$ then they will proceed
with stage two and if stage one results in $a\ket{11}+b\ket{00}$, they will
abandon the entangled pair. Such a modification would also be probabilistic
and result in 1 bit per qubit for teleportation. However, we point out that
the probability of 1 bit per qubit in such a modified Bennett’s protocol would
be stuck at $\frac{1}{2}$. Whereas, our reset procedure allows this
probability to be $\frac{1}{2}+|ab|^{2}$ which is greater than $\frac{1}{2}$
for all practical purposes.

The probability of success for the reset operation at every attempt changes
with the amplitudes $a$ and $b$ and is given by a recursive relationship. If
$R_{0},R_{1},R_{2},\ldots$ represents the reset successive reset attempts then
the probability of success of each reset is given by,

$\begin{split}&P(R_{0})=2|ab|^{2}\\\ &P(R_{1})=2|a_{1}b_{1}|^{2}\mbox{ where
}a_{1}=\frac{a^{2}}{\sqrt{|a^{2}|^{2}+|b^{2}|^{2}}}\mbox{ and
}b_{1}=\frac{b^{2}}{\sqrt{|a^{2}|^{2}+|b^{2}|^{2}}}\\\
&P(R_{2})=2|a_{2}b_{2}|^{2}\mbox{ where
}a_{2}=\frac{a_{1}^{2}}{\sqrt{|a_{1}^{2}|^{2}+|b_{1}^{2}|^{2}}}\mbox{ and
}b_{2}=\frac{b_{1}^{2}}{\sqrt{|a_{1}^{2}|^{2}+|b_{1}^{2}|^{2}}}\\\ &\mbox{and
so on.}\end{split}$ (5)

Therefore, the probability that Alice would need three reset attempts is given
by $(1-P(R_{0}))(1-P(R_{1}))P(R_{2})$. For a state such as
$\frac{\ket{0}+\ket{1}}{\sqrt{2}}$ this would be $\frac{1}{8}$.

An important implication of Equation 4 is that when $|ab|^{2}>0$ there is a
non-zero probability of a successful reset. The equality, $|ab|^{2}=0$, holds
only when one of the amplitudes $a$ or $b$ = 0, a corner case. As a result,
the above protocol breaks the symmetry between the four cases of the
conventional teleportation protocol [1]. More precisely, in the conventional
teleportation protocol, there is an equal probability for Bob to end up in any
of the four states: $\ket{\phi_{0}}=a\ket{0}+b\ket{1}$,
$\ket{\phi_{1}}=a\ket{0}-b\ket{1}$, $\ket{\phi_{2}}=b\ket{0}+a\ket{1}$, and
$\ket{\phi_{3}}=b\ket{0}-a\ket{1}$. Consequently, two classical bits were
needed for teleportation. However, with the proposed reset operation the
relationship between the probabilities of the states that Bob sees is given
by,

$P(\ket{\phi_{0}})+P(\ket{\phi_{1}})>P(\ket{\phi_{2}})+P(\ket{\phi_{3}})$ (6)

For a moment only consider the first stage of the teleportation protocol and
the reset operation. Assume that Alice has not transmitted any information to
Bob. We see that, the reset operation also implies that when $a,b\neq 0$,
Alice can unilaterally force the shared entanglement,
$\frac{\ket{00}+\ket{11}}{\sqrt{2}}$ to be converted to $a\ket{00}+b\ket{11}$
with a probability higher than $\frac{1}{2}$ and no communication with Bob. If
the qubit $\ket{\phi}$ is deliberately constructed by Alice and the
information Alice wants to transmit is encoded in the values of $a$ and $b$
rather than the phase difference between them, then Alice has successfully
induced a bias in Bob’s qubit.

The ability of one party to, unilaterally, induce a bias in a shared entangled
pair gives a party wanting to cheat, in a cryptographic protocol, an
advantage. For example, several quantum key agreement protocols have been
proposed in literature [20, 21, 22, 23, 24, 25]. These protocols are designed
such that both the communicating parties make equal contribution to the final
agreed key. This is in contrast to traditional quantum key distribution
protocols such as BB84 where one party determines the entire key and securely
distributes it to the other party. If the quantum key agreement protocol is
based on entanglement then one of the cheating parties can induce a bias in
the final key stream by introducing an appropriate $\ket{\phi}$ in the system.
The simplest example of a weak quantum key agreement protocol is the E91
protocol where both parties make random measurements on maximally entangled
pairs of qubits. If these maximally entangled pairs are distributed by a
central authority then Alice (cheating party) can introduce
$\ket{\phi}=a\ket{0}+b\ket{1}$ such that $|a|^{2}>>|b|^{2}$ resulting in a
biased measurement result, at Bob’s end, of her own choosing. Therefore, the
asymmetry caused by the reset operation gives Alice more power than Bob and is
devastating for cryptographic protocols that use entanglement and rely on the
powers of the communicating parties being symmetric for security.

In this paper, we have only considered the ideal case where the shared
entangled pair is maximally entangled. Investigation into how the reset
procedure would proceed in the case of non-maximally entangled states, mixed
states, non-ideal measurements and channel errors are left as future work.
Entanglement distillation [26] could be used to mitigate the effects of these
non-ideal cases.

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