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

arXiv: https://arxiv.org/abs/2211.13035 · HTML: https://mlsanity.com/paper/2211.13035/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 75 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Video Classification with Channel-Separated Convolutional Networks](https://mlsanity.com/paper/1904.02811/) (2019)
- [TEA: Temporal Excitation and Aggregation for Action Recognition](https://mlsanity.com/paper/2004.01398/) (2020)
- [STM: SpatioTemporal and Motion Encoding for Action Recognition](https://mlsanity.com/paper/1908.02486/) (2019)
- [TEINet: Towards an Efficient Architecture for Video Recognition](https://mlsanity.com/paper/1911.09435/) (2019)
- [MotionSqueeze: Neural Motion Feature Learning for Video Understanding](https://mlsanity.com/paper/2007.09933/) (2020)
- [GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing](https://mlsanity.com/paper/1907.04433/) (2019)
- [Hidden Two-Stream Convolutional Networks for Action Recognition](https://mlsanity.com/paper/1704.00389/) (2017)
- [A robust and efficient video representation for action recognition](https://mlsanity.com/paper/1504.05524/) (2015)
- [Deception Detection in Videos](https://mlsanity.com/paper/1712.04415/) (2017)
- [A Deep Learning Approach for Multimodal Deception Detection](https://mlsanity.com/paper/1803.00344/) (2018)
- [Face-Focused Cross-Stream Network for Deception Detection in Videos](https://mlsanity.com/paper/1812.04429/) (2018)
- [Introducing Representations of Facial Affect in Automated Multimodal Deception Detection](https://mlsanity.com/paper/2008.13369/) (2020)
- [The Mafiascum Dataset: A Large Text Corpus for Deception Detection](https://mlsanity.com/paper/1811.07851/) (2018)
- [The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection](https://mlsanity.com/paper/1903.04484/) (2019)
- [Deception Detection by 2D-to-3D Face Reconstruction from Videos](https://mlsanity.com/paper/1812.10558/) (2018)

## Built on by (most cited first, 2 of 2)

- [Enhancing Lie Detection Accuracy: A Comparative Study of Classic ML, CNN, and GCN Models using Audio-Visual Features](https://mlsanity.com/paper/2411.08885/) (2024)
- [Deception Detection in Dyadic Exchanges Using Multimodal Machine Learning: A Study on a Swedish Cohort](https://mlsanity.com/paper/2506.21429/) (2025)

## Abstract (arXiv)

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.