Machine Learning based Lie Detector applied to a Collected and Annotated Dataset

• URL: http://arxiv.org/abs/2104.12345v1
• Date: Mon, 26 Apr 2021 04:48:42 GMT
• Title: Machine Learning based Lie Detector applied to a Collected and Annotated Dataset
• Authors: Nuria Rodriguez-Diaz, Decky Aspandi, Federico Sukno, Xavier Binefa
• Abstract summary: We have collected a dataset that contains annotated images and 3D information of different participants faces during a card game that incentivises the lying. Using our collected dataset, we evaluated several types of machine learning based lie detector through generalize, personal and cross lie experiments. In these experiments, we showed the superiority of deep learning based model in recognizing the lie with best accuracy of 57% for generalized task and 63% when dealing with a single participant.
• Score: 1.3007851628964147
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lie detection is considered a concern for everyone in their day to day life given its impact on human interactions. Hence, people are normally not only pay attention to what their interlocutors are saying but also try to inspect their visual appearances, including faces, to find any signs that indicate whether the person is telling the truth or not. Unfortunately to date, the automatic lie detection, which may help us to understand this lying characteristics are still fairly limited. Mainly due to lack of a lie dataset and corresponding evaluations. In this work, we have collected a dataset that contains annotated images and 3D information of different participants faces during a card game that incentivise the lying. Using our collected dataset, we evaluated several types of machine learning based lie detector through generalize, personal and cross lie lie experiments. In these experiments, we showed the superiority of deep learning based model in recognizing the lie with best accuracy of 57\% for generalized task and 63\% when dealing with a single participant. Finally, we also highlight the limitation of the deep learning based lie detector when dealing with different types of lie tasks.

Related papers

• When lies are mostly truthful: automated verbal deception detection for embedded lies [0.3867363075280544]
  We collected a novel dataset of 2,088 truthful and deceptive statements with annotated embedded lies. We show that a fined-tuned language model (Llama-3-8B) can classify truthful statements and those containing embedded lies with 64% accuracy.
  arXiv  Detail & Related papers  (2025-01-13T11:16:05Z)
• Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method [77.65459419417533]
  We put face forgery in a semantic context and define that computational methods that alter semantic face attributes are sources of face forgery. We construct a large face forgery image dataset, where each image is associated with a set of labels organized in a hierarchical graph. We propose a semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task.
  arXiv  Detail & Related papers  (2024-05-14T10:24:19Z)
• A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment [76.04306818209753]
  We introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform. This dataset comprises approximately two thousand workers, one million tasks, and six million annotations. We evaluate the effectiveness of several representative truth inference algorithms on this dataset.
  arXiv  Detail & Related papers  (2024-03-10T16:00:41Z)
• Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning [48.11096630736294]
  We extend deception detection to deception reasoning. Specifically, we provide potential lies and basic facts and then analyze why this sentence may be a lie. This paper presents our initial attempts at this task, including constructing a dataset and defining evaluation metrics.
  arXiv  Detail & Related papers  (2024-02-18T02:52:54Z)
• SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection [9.20397189600732]
  This research explores the problem of deception through the lens of psychology. We propose a novel framework for deception detection leveraging NLP techniques. We present a novel multi-task learning pipeline that leverages the dataless merging of fine-tuned language models.
  arXiv  Detail & Related papers  (2023-12-01T02:13:25Z)
• Localizing Lying in Llama: Understanding Instructed Dishonesty on True-False Questions Through Prompting, Probing, and Patching [0.0]
  Large language models (LLMs) demonstrate significant knowledge through their outputs, though it is often unclear whether false outputs are due to a lack of knowledge or dishonesty. In this paper, we investigate instructed dishonesty, wherein we explicitly prompt LLaMA-2-70b-chat to lie. We perform prompt engineering to find which prompts best induce lying behavior, and then use mechanistic interpretability approaches to localize where in the network this behavior occurs.
  arXiv  Detail & Related papers  (2023-11-25T22:41:23Z)
• To Tell The Truth: Language of Deception and Language Models [6.80186731352488]
  We analyze a novel TV game show data where conversations in a high-stake environment result in lies. We investigate the manifestation of potentially verifiable language cues of deception in the presence of objective truth. We show that there exists a class of detectors (algorithms) that have similar truth detection performance compared to human subjects.
  arXiv  Detail & Related papers  (2023-11-13T05:40:11Z)
• Discovering Latent Knowledge in Language Models Without Supervision [72.95136739040676]
  Existing techniques for training language models can be misaligned with the truth. We propose directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models.
  arXiv  Detail & Related papers  (2022-12-07T18:17:56Z)
• Deception Detection in Videos using the Facial Action Coding System [4.641678530055641]
  In our approach, we extract facial action units using the facial action coding system which we use as parameters for training a deep learning model. We specifically use long short-term memory (LSTM) which we trained using the real-life trial dataset. We also tested cross-dataset validation using the Real-life trial dataset, the Silesian Deception dataset, and the Bag-of-lies Deception dataset.
  arXiv  Detail & Related papers  (2021-05-28T08:10:21Z)
• Hidden Footprints: Learning Contextual Walkability from 3D Human Trails [70.01257397390361]
  Current datasets only tell you where people are, not where they could be. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss.
  arXiv  Detail & Related papers  (2020-08-19T23:19:08Z)
• Exploratory Machine Learning with Unknown Unknowns [60.78953456742171]
  We study a new problem setting in which there are unknown classes in the training data misperceived as other labels. We propose the exploratory machine learning, which examines and investigates training data by actively augmenting the feature space to discover potentially hidden classes.
  arXiv  Detail & Related papers  (2020-02-05T02:06:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.