3,655 research outputs found
Automated Deception Detection from Videos: Using End-to-End Learning Based High-Level Features and Classification Approaches
Deception detection is an interdisciplinary field attracting researchers from
psychology, criminology, computer science, and economics. We propose a
multimodal approach combining deep learning and discriminative models for
automated deception detection. Using video modalities, we employ convolutional
end-to-end learning to analyze gaze, head pose, and facial expressions,
achieving promising results compared to state-of-the-art methods. Due to
limited training data, we also utilize discriminative models for deception
detection. Although sequence-to-class approaches are explored, discriminative
models outperform them due to data scarcity. Our approach is evaluated on five
datasets, including a new Rolling-Dice Experiment motivated by economic
factors. Results indicate that facial expressions outperform gaze and head
pose, and combining modalities with feature selection enhances detection
performance. Differences in expressed features across datasets emphasize the
importance of scenario-specific training data and the influence of context on
deceptive behavior. Cross-dataset experiments reinforce these findings. Despite
the challenges posed by low-stake datasets, including the Rolling-Dice
Experiment, deception detection performance exceeds chance levels. Our proposed
multimodal approach and comprehensive evaluation shed light on the potential of
automating deception detection from video modalities, opening avenues for
future research.Comment: 29 pages, 17 figures (19 if counting subfigures
LoRA-like Calibration for Multimodal Deception Detection using ATSFace Data
Recently, deception detection on human videos is an eye-catching techniques
and can serve lots applications. AI model in this domain demonstrates the high
accuracy, but AI tends to be a non-interpretable black box. We introduce an
attention-aware neural network addressing challenges inherent in video data and
deception dynamics. This model, through its continuous assessment of visual,
audio, and text features, pinpoints deceptive cues. We employ a multimodal
fusion strategy that enhances accuracy; our approach yields a 92\% accuracy
rate on a real-life trial dataset. Most important of all, the model indicates
the attention focus in the videos, providing valuable insights on deception
cues. Hence, our method adeptly detects deceit and elucidates the underlying
process. We further enriched our study with an experiment involving students
answering questions either truthfully or deceitfully, resulting in a new
dataset of 309 video clips, named ATSFace. Using this, we also introduced a
calibration method, which is inspired by Low-Rank Adaptation (LoRA), to refine
individual-based deception detection accuracy.Comment: 10 pages, 9 figure
Detection of Deception in a Virtual World
This work explores the role of multimodal cues in detection of deception in a virtual world, an online community of World of Warcraft players. Case studies from a five-year ethnography are presented in three categories: small-scale deception in text, deception by avoidance, and large-scale deception in game-external modes. Each case study is analyzed in terms of how the affordances of the medium enabled or hampered deception as well as how the members of the community ultimately detected the deception. The ramifications of deception on the community are discussed, as well as the need for researchers to have a deep community knowledge when attempting to understand the role of deception in a complex society. Finally, recommendations are given for assessment of behavior in virtual worlds and the unique considerations that investigators must give to the rules and procedures of online communities.</jats:p
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection
Detecting whether a news article is fake or genuine is a crucial task in
today's digital world where it's easy to create and spread a misleading news
article. This is especially true of news stories shared on social media since
they don't undergo any stringent journalistic checking associated with main
stream media. Given the inherent human tendency to share information with their
social connections at a mouse-click, fake news articles masquerading as real
ones, tend to spread widely and virally. The presence of echo chambers (people
sharing same beliefs) in social networks, only adds to this problem of
wide-spread existence of fake news on social media. In this paper, we tackle
the problem of fake news detection from social media by exploiting the very
presence of echo chambers that exist within the social network of users to
obtain an efficient and informative latent representation of the news article.
By modeling the echo-chambers as closely-connected communities within the
social network, we represent a news article as a 3-mode tensor of the structure
- and propose a tensor factorization based method to
encode the news article in a latent embedding space preserving the community
structure. We also propose an extension of the above method, which jointly
models the community and content information of the news article through a
coupled matrix-tensor factorization framework. We empirically demonstrate the
efficacy of our method for the task of Fake News Detection over two real-world
datasets. Further, we validate the generalization of the resulting embeddings
over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and
\textbf{2)} Collaborative News Recommendation. Our proposed method outperforms
appropriate baselines for both the tasks, establishing its generalization.Comment: Presented at ASONAM'1
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