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ARXIV:2603.26064 · DECEPTION DETECTION · SUBMITTED 30 MAR · 22:25 UTC · FRESHNESS STALE
ARXIV:2603.26064DECEPTION DETECTIONSUBMITTED 30 MAR · 22:25 UTCFRESHNESS STALEPeiyuan Jiang · Yao Liu · Yanglei Gan · Jiaye Yang · Lu Liu · Daibing Yao · +1 at arXiv
A multimodal dataset and distillation framework for non-contact deception detection, leveraging physiological cues to improve accuracy.
Opportunity summary
Pain A multimodal dataset and distillation framework for non-contact deception detection, leveraging physiological cues to improve accuracy.
Evidence 57 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A multimodal dataset and distillation framework for non-contact deception detection, leveraging physiological cues to improve accuracy. In contrast, galvanic skin response (GSR) provides more reliable physiological cues and has been widely used in contact-based…
Non-contact automatic deception detection remains challenging because visual and auditory deception cues often lack stable cross-subject patterns. In contrast, galvanic skin response (GSR) provides more reliable physiological cues and has been widely used in…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments and visualizations show that GPD outperforms existing methods and achieves state-of-the-art performance on both deception detection and concealed-digit identification. Code availability is…
Deception Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A multimodal dataset and distillation framework for non-contact deception detection, leveraging physiological cues to improve accuracy.
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10.48550/arXiv.2603.26064A multimodal dataset and distillation framework for non-contact deception detection, leveraging physiological cues to improve accuracy.
Abstract
Non-contact automatic deception detection remains challenging because visual and auditory deception cues often lack stable cross-subject patterns. In contrast, galvanic skin response (GSR) provides more reliable physiological cues and has been widely used in contact-based deception detection. In this work, we leverage stable deception-related knowledge in GSR to guide representation learning in non-contact modalities through cross-modal knowledge distillation. A key obstacle, however, is the lack of a suitable dataset for this setting. To address this, we introduce MuDD, a large-scale Multimodal Deception Detection dataset containing recordings from 130 participants over 690 minutes. In addition to video, audio, and GSR, MuDD also provides Photoplethysmography, heart rate, and personality traits, supporting broader scientific studies of deception. Based on this dataset, we propose GSR-guided Progressive Distillation (GPD), a cross-modal distillation framework for mitigating the negative transfer caused by the large modality mismatch between GSR and non-contact signals. The core innovation of GPD is the integration of progressive feature-level and digit-level distillation with dynamic routing, which allows the model to adaptively determine how teacher knowledge should be transferred during training, leading to more stable cross-modal knowledge transfer. Extensive experiments and visualizations show that GPD outperforms existing methods and achieves state-of-the-art performance on both deception detection and concealed-digit identification.
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Proof status
unverified57 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A multimodal dataset and distillation framework for non-contact deception detection, leveraging physiological cues to improve accuracy. In contrast, galvanic skin response (GSR) provides more reliable physiological cues and has been widely used in contact-based deception detecti...
METHOD
Non-contact automatic deception detection remains challenging because visual and auditory deception cues often lack stable cross-subject patterns. In contrast, galvanic skin response (GSR) provides more reliable physiological cues and has been widely used in contact-based decept...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experiments and visualizations show that GPD outperforms existing methods and achieves state-of-the-art performance on both deception detection and concealed-digit identification. Code availabil...
WHY NOW
Deception Detection moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
In addition to video, audio, and GSR, MuDD also provides Photoplethysmography, heart rate, and personality traits, supporting broader scientific studies of deception.
The abstract and dataset description explicitly state the size and modalities of the MuDD dataset.
partial
Based on this dataset, we propose GSR-guided Progressive Distillation (GPD), a cross-modal distillation framework for non-contact deception detection.
The abstract and introduction clearly define GPD and its purpose.
partial
The core innovation of GPD is the integration of progressive feature-level and digit-level distillation with dynamic routing, which allows the model to adaptively determine how teacher knowledge should be transferred during training, leading to more stable cross-modal knowledge transfer.
The abstract and method description detail the core components of GPD.
partial
Extensive experiments and visualizations show that GPD outperforms existing methods and achieves state-of-the-art performance on both deception detection and concealed-digit identification.
The abstract states that GPD achieves state-of-the-art performance, and the results section indicates comparisons against baselines.
partial
Non-contact automatic deception detection remains challenging because visual and auditory deception cues often lack stable cross-subject patterns. In contrast, galvanic skin response (GSR) provides more reliable physiological cues and has been widely used in contact-based deception detection.
The abstract highlights the advantage of GSR over non-contact modalities for deception detection.
partial
GPD consists of four key components: a teacher network, a student network, a progressive knowledge distillation module, and a digit-level evidence aggregation module. The teacher network takes GSR as input, while the student network takes visual and audio features as input.
The description of GPD's components is provided in the text.
partial
GPD distills two types of teacher knowledge: feature-level physiological representations and digit-level concealed-item evidence.
The abstract and method description explicitly mention the two types of knowledge being distilled.
partial
In addition to video, audio, and GSR, MuDD also provides Photoplethysmography, heart rate, and personality traits, supporting broader scientific studies of deception.
The abstract explicitly states the size and content of the MuDD dataset, and this is further detailed in the 'MuDD Dataset' section.
partial
Based on this dataset, we propose GSR-guided Progressive Distillation (GPD), a cross-modal distillation framework for non-contact deception detection.
The abstract and introduction clearly define GPD as a method for non-contact deception detection using GSR guidance.
partial
The core innovation of GPD is the integration of progressive feature-level and digit-level distillation with dynamic routing, which allows the model to adaptively determine how teacher knowledge should be transferred during training, leading to more stable cross-modal knowledge transfer.
The abstract and description of GPD highlight these key components and their adaptive nature.
partial
Extensive experiments and visualizations show that GPD outperforms existing methods and achieves state-of-the-art performance on both deception detection and concealed-digit identification.
The abstract states that GPD achieves state-of-the-art performance, and the 'Main Results' section indicates comparisons against existing methods.
partial
Extensive experiments and visualizations show that GPD outperforms existing methods and achieves state-of-the-art performance on both deception detection and concealed-digit identification.
The abstract explicitly states that GPD outperforms existing methods, and the 'Main Results' section is dedicated to comparing it against baselines.
partial
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A multimodal dataset and distillation framework for non-contact deception detection, leveraging physiological cues to improve accuracy.
Segment
Deception Detection
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Commercial read
7.0/10 public viability
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reason
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proof status
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Evidence coverage
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57 refs / 3 sources / 50% coverage
stale
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Build readiness
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passport absent
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Artifact maturity
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Technical feasibility
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Evidence
57 references, 3 sources, 50% evidence coverage.
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Write integration checklist from prototype path and target workflow.
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