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arxiv: 2603.23916 · v2 · submitted 2026-03-25 · 💻 cs.CV · cs.AI

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DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning

Chunmei Zhu, Dongliang Zhu, Hui Ma, Jiajian Huang, Jiayu Zhang, Xiaochun Cao, Zitong Yu

Authors on Pith no claims yet

Pith reviewed 2026-05-15 01:05 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords deception detectionmultimodal learningmulticultural datasetsreasoning chainscross-domain generalizationknowledge distillationaudiovisual cues
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The pith

DecepGPT augments existing deception benchmarks with cue descriptions and reasoning chains, adds a large multicultural dataset, and introduces two modules to reach state-of-the-art detection that transfers across domains and cultures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to make multimodal deception detection more reliable for forensics by replacing binary labels with structured reasoning outputs and by expanding limited training data. It augments prior benchmarks with explicit cue-level descriptions and step-by-step reasoning chains so models can emit auditable reports. It also releases T4-Deception, a 1695-sample collection drawn from the same television game format run in four countries, providing the largest non-laboratory multicultural resource to date. Two new components, Stabilized Individuality-Commonality Synergy and Distilled Modality Consistency, are designed to refine fused representations and prevent unimodal shortcuts under small-data regimes. Experiments show these changes together deliver leading accuracy on both established benchmarks and the new dataset while improving cross-cultural transfer.

Core claim

By converting binary-labeled deception videos into schema-augmented datasets that contain cue descriptions and explicit reasoning chains, and by training with Stabilized Individuality-Commonality Synergy plus Distilled Modality Consistency on the new T4-Deception collection, the method produces verifiable multimodal predictions that outperform prior work in both in-domain accuracy and cross-domain generalization across cultural contexts.

What carries the argument

Stabilized Individuality-Commonality Synergy (SICS) that combines learnable global priors with sample-adaptive residuals followed by polarity-aware recalibration, together with Distilled Modality Consistency (DMC) that uses knowledge distillation to align unimodal and fused predictions.

If this is right

  • Models can output auditable step-by-step reports instead of opaque binary decisions in security and legal settings.
  • Training on the multicultural T4-Deception set reduces reliance on culture-specific shortcuts.
  • The two modules stabilize learning when labeled deception data remain scarce.
  • Cross-domain transfer improves without requiring new labeled samples from every target domain.
  • Knowledge-distillation alignment discourages any single modality from dominating the final prediction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same cue-augmentation pattern could be applied to other high-stakes multimodal tasks such as medical diagnosis or misinformation detection to increase explainability.
  • If the reasoning chains prove robust, they could serve as supervision for smaller models in resource-constrained environments.
  • Real-world deployment would still require testing on continuous video streams rather than the short clips used here.

Load-bearing premise

The manually supplied cue descriptions and reasoning chains added to the datasets accurately reflect real deception signals rather than annotator bias or post-hoc justification.

What would settle it

Re-train the same architecture on the original unaugmented benchmarks without the added cue descriptions and reasoning chains, then measure whether cross-domain and cross-cultural accuracy drops to the level of prior methods.

Figures

Figures reproduced from arXiv: 2603.23916 by Chunmei Zhu, Dongliang Zhu, Hui Ma, Jiajian Huang, Jiayu Zhang, Xiaochun Cao, Zitong Yu.

Figure 1
Figure 1. Figure 1: Comparison between traditional and our auditable method. (a) Traditional methods map [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of reasoning dataset generation pipeline. The pipeline adopts a Human-in-the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Dataset statistics of T4-Deception. We illustrate: (a) distribution of identities, where each [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of our auditable audiovisual deception detection framework. A video encoder [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Projector gradi￾ent dynamics analysis during training. 5 Visualization and Interpretability Analysis We analyzes the internal mechanisms of the SICS Adapter and DMC Regularizer from two aspects: (i) unimodal shortcut mitigation in the DMC Regularizer. and (ii) representation stabilization in the SICS Adapter; We also assess the fidelity of the generated auditable evidence, verifying that it is grounded in … view at source ↗
Figure 6
Figure 6. Figure 6: Visualization analysis of the SICS Adapter. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Quantitative audit analysis. (a) distribution of cue correctness and reasoning quality cate [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison of a success case vs. a failure case with human audit tags. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
read the original abstract

Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-stakes settings, investigators need verifiable evidence connecting audiovisual cues to final decisions, along with reliable generalization across domains and cultural contexts. However, existing benchmarks provide only binary labels without intermediate reasoning cues. Datasets are also small with limited scenario coverage, leading to shortcut learning. We address these issues through three contributions. First, we construct reasoning datasets by augmenting existing benchmarks with structured cue-level descriptions and reasoning chains, enabling model output auditable reports. Second, we release T4-Deception, a multicultural dataset based on the unified ``To Tell The Truth'' television format implemented across four countries. With 1695 samples, it is the largest non-laboratory deception detection dataset. Third, we propose two modules for robust learning under small-data conditions. Stabilized Individuality-Commonality Synergy (SICS) refines multimodal representations by synergizing learnable global priors with sample-adaptive residuals, followed by a polarity-aware adjustment that bi-directionally recalibrates representations. Distilled Modality Consistency (DMC) aligns modality-specific predictions with the fused multimodal predictions via knowledge distillation to prevent unimodal shortcut learning. Experiments on three established benchmarks and our novel dataset demonstrate that our method achieves state-of-the-art performance in both in-domain and cross-domain scenarios, while exhibiting superior transferability across diverse cultural contexts. The datasets and codes will be released.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces DecepGPT, a multimodal deception detection framework that augments existing benchmarks with manually constructed cue-level descriptions and reasoning chains for auditable outputs, releases the T4-Deception multicultural dataset (1695 samples across four countries in a unified 'To Tell The Truth' format), and proposes two modules—Stabilized Individuality-Commonality Synergy (SICS) for refining multimodal representations via global priors and adaptive residuals, and Distilled Modality Consistency (DMC) for aligning unimodal and fused predictions via knowledge distillation. It claims state-of-the-art in-domain and cross-domain performance with superior cultural transferability on three established benchmarks plus the new dataset.

Significance. If the manual cue annotations are shown to capture reproducible deception signals independent of annotator bias and the performance claims are supported by ablations and metrics, the work would advance verifiable, generalizable deception detection by addressing binary-label limitations and shortcut learning, while providing the largest non-laboratory multicultural dataset for the field.

major comments (1)
  1. [§3] §3 (Dataset Construction): The augmentation of benchmarks with structured cue-level descriptions and reasoning chains is presented as the first contribution enabling auditable reports, yet no inter-annotator agreement scores, expert validation, or ablation results (e.g., performance with vs. without the added chains on original binary labels) are reported. This is load-bearing for the SOTA and cross-cultural transfer claims, as the SICS/DMC modules and T4-Deception results may simply reproduce the annotation schema rather than independent audiovisual signals.
minor comments (2)
  1. [Abstract] The abstract asserts SOTA performance without any quantitative metrics, error bars, or implementation details; these should be summarized upfront even if detailed in §4.
  2. [§4] Notation for SICS (e.g., the polarity-aware adjustment) and DMC (distillation loss) could be clarified with explicit equations in §4 to aid reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback, particularly on the dataset construction. We address the concern point by point below and will incorporate the suggested validations in the revised manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Dataset Construction): The augmentation of benchmarks with structured cue-level descriptions and reasoning chains is presented as the first contribution enabling auditable reports, yet no inter-annotator agreement scores, expert validation, or ablation results (e.g., performance with vs. without the added chains on original binary labels) are reported. This is load-bearing for the SOTA and cross-cultural transfer claims, as the SICS/DMC modules and T4-Deception results may simply reproduce the annotation schema rather than independent audiovisual signals.

    Authors: We agree that the absence of reported inter-annotator agreement (IAA) scores, expert validation details, and targeted ablations weakens the substantiation of the cue-augmentation contribution. The cue-level descriptions and reasoning chains were constructed by trained annotators following a protocol derived from established deception literature (e.g., verbal and nonverbal cue taxonomies), but these details and metrics were omitted from the initial submission. In the revised manuscript we will add: (1) IAA scores (Cohen’s kappa and percentage agreement) computed across multiple annotators for both cue descriptions and reasoning chains; (2) a brief description of the annotation guidelines and any expert review performed; and (3) ablation experiments that train and evaluate the full SICS+DMC pipeline on the original binary labels versus the augmented reasoning-chain versions of the same benchmarks. These results will isolate whether performance gains derive from audiovisual signals or merely from the annotation schema, directly addressing the load-bearing concern for the reported SOTA and cross-cultural transfer claims. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on independent dataset evaluations

full rationale

The paper constructs augmented reasoning datasets via manual cue annotations and introduces SICS and DMC modules for multimodal fusion, then reports SOTA results on three benchmarks plus the new T4-Deception dataset. No equations, derivations, or fitted parameters are shown that reduce by construction to the inputs. No self-citations are invoked as load-bearing uniqueness theorems, and no ansatz or renaming of known results occurs. The central performance and transferability claims are supported by cross-dataset experiments rather than self-referential definitions, making the derivation chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the work relies on standard assumptions of multimodal fusion and knowledge distillation.

pith-pipeline@v0.9.0 · 5582 in / 1036 out tokens · 38167 ms · 2026-05-15T01:05:36.984856+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

34 extracted references · 7 canonical work pages · 4 internal anchors

  1. [1]

    Deception detection using real-life trial data

    Ver´ onica P´ erez-Rosas, Mohamed Abouelenien, Rada Mihalcea, and Mihai Burzo. Deception detection using real-life trial data. InProceedings of the 2015 ACM on international conference on multimodal interaction, pages 59–66, 2015

    2015

  2. [2]

    Audio-visual deception detection: Dolos dataset and parameter-efficient crossmodal learning

    Xiaobao Guo, Nithish Muthuchamy Selvaraj, Zitong Yu, Adams Wai-Kin Kong, Bingquan Shen, and Alex Kot. Audio-visual deception detection: Dolos dataset and parameter-efficient crossmodal learning. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 22135–22145, 2023

    2023

  3. [3]

    Accuracy of deception judgments.Personality and social psychology Review, 10(3):214–234, 2006

    Charles F Bond Jr and Bella M DePaulo. Accuracy of deception judgments.Personality and social psychology Review, 10(3):214–234, 2006

    2006

  4. [4]

    Verbal and nonverbal clues for real-life deception detection

    Ver´ onica P´ erez-Rosas, Mohamed Abouelenien, Rada Mihalcea, Yao Xiao, CJ Linton, and Mihai Burzo. Verbal and nonverbal clues for real-life deception detection. InProceedings of the 2015 conference on empirical methods in natural language processing, pages 2336–2346, 2015

    2015

  5. [5]

    Facialcuenet: unmasking deception-an interpretable model for criminal interrogation using facial expressions: Iy kim et al.Applied Intelligence, 53(22):27413– 27427, 2023

    Borum Nam, Joo Young Kim, Beomjun Bark, Yeongmyeong Kim, Jiyoon Kim, Soon Won So, Hyung Youn Choi, and In Young Kim. Facialcuenet: unmasking deception-an interpretable model for criminal interrogation using facial expressions: Iy kim et al.Applied Intelligence, 53(22):27413– 27427, 2023

    2023

  6. [6]

    Cross-cultural deception detection

    Ver´ onica P´ erez-Rosas and Rada Mihalcea. Cross-cultural deception detection. InProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 440–445, 2014

    2014

  7. [7]

    Bag-of-lies: A multimodal dataset for deception detection

    Viresh Gupta, Mohit Agarwal, Manik Arora, Tanmoy Chakraborty, Richa Singh, and Mayank Vatsa. Bag-of-lies: A multimodal dataset for deception detection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 1–8, 2019

    2019

  8. [8]

    Shortcut learning in deep neural networks.Nature Machine Intelli- gence, 2(11):665–673

    Robert Geirhos, J¨ orn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. Shortcut learning in deep neural networks.Nature Machine Intelli- gence, 2(11):665–673

  9. [9]

    Deception detection in videos

    Zhe Wu, Bharat Singh, Larry Davis, and V Subrahmanian. Deception detection in videos. InProceed- ings of the AAAI conference on artificial intelligence, volume 32, 2018

    2018

  10. [10]

    Benchmarking cross-domain audio-visual deception detection.arXiv preprint arXiv:2405.06995, 2024

    Xiaobao Guo, Zitong Yu, Nithish Muthuchamy Selvaraj, Bingquan Shen, Adams Wai-Kin Kong, and Alex C Kot. Benchmarking cross-domain audio-visual deception detection.arXiv preprint arXiv:2405.06995, 2024

    work page arXiv 2024

  11. [11]

    Visual instruction tuning

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. volume 36, pages 34892–34916, 2023

    2023

  12. [12]

    Hidden in plain sight: Evaluation of the deception detection capabilities of llms in multimodal settings

    Md Messal Monem Miah, Adrita Anika, Xi Shi, and Ruihong Huang. Hidden in plain sight: Evaluation of the deception detection capabilities of llms in multimodal settings. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31013–31034, 2025

    2025

  13. [13]

    Misa: Modality-invariant and-specific representations for multimodal sentiment analysis

    Devamanyu Hazarika, Roger Zimmermann, and Soujanya Poria. Misa: Modality-invariant and-specific representations for multimodal sentiment analysis. InProceedings of the 28th ACM international conference on multimedia, pages 1122–1131, 2020

    2020

  14. [14]

    Domain separation networks.Advances in neural information processing systems, 29, 2016

    Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan. Domain separation networks.Advances in neural information processing systems, 29, 2016. DecepGPT 13

    2016

  15. [15]

    Learning discriminative multi- relation representations for multimodal sentiment analysis.Information Sciences, 641:119125, 2023

    Zemin Tang, Qi Xiao, Xu Zhou, Yangfan Li, Cen Chen, and Kenli Li. Learning discriminative multi- relation representations for multimodal sentiment analysis.Information Sciences, 641:119125, 2023

    2023

  16. [16]

    Gated Multimodal Units for Information Fusion

    John Arevalo, Thamar Solorio, Manuel Montes-y G´ omez, and Fabio A Gonz´ alez. Gated multimodal units for information fusion.arXiv preprint arXiv:1702.01992, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  17. [17]

    Balanced multimodal learning via on-the-fly gradient modulation

    Xiaokang Peng, Yake Wei, Andong Deng, Dong Wang, and Di Hu. Balanced multimodal learning via on-the-fly gradient modulation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8238–8247, 2022

    2022

  18. [18]

    Moddrop: adaptive multi- modal gesture recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(8):1692–1706, 2015

    Natalia Neverova, Christian Wolf, Graham Taylor, and Florian Nebout. Moddrop: adaptive multi- modal gesture recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(8):1692–1706, 2015

    2015

  19. [19]

    Adaptive unimodal regulation for balanced multimodal information acquisition

    Chengxiang Huang, Yake Wei, Zequn Yang, and Di Hu. Adaptive unimodal regulation for balanced multimodal information acquisition. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 25854–25863, 2025

    2025

  20. [20]

    Miami university deception detection database.Behavior research methods, 51(1):429–439, 2019

    E Paige Lloyd, Jason C Deska, Kurt Hugenberg, Allen R McConnell, Brandon T Humphrey, and Jonathan W Kunstman. Miami university deception detection database.Behavior research methods, 51(1):429–439, 2019

    2019

  21. [21]

    Deception detection and remote physiological monitoring: A dataset and baseline experimental results

    Jeremy Speth, Nathan Vance, Adam Czajka, Kevin W Bowyer, Diane Wright, and Patrick Flynn. Deception detection and remote physiological monitoring: A dataset and baseline experimental results. In2021 IEEE International Joint Conference on Biometrics, pages 1–8, 2021

    2021

  22. [22]

    Box of lies: Multimodal deception detection in dialogues

    Felix Soldner, Ver´ onica P´ erez-Rosas, and Rada Mihalcea. Box of lies: Multimodal deception detection in dialogues. InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1768–1777, 2019

    2019

  23. [23]

    Affectgpt: Dataset and framework for explainable multimodal emotion recognition.arXiv preprint arXiv:2407.07653, 2024

    Zheng Lian, Haiyang Sun, Licai Sun, Jiangyan Yi, Bin Liu, and Jianhua Tao. Affectgpt: Dataset and framework for explainable multimodal emotion recognition.arXiv preprint arXiv:2407.07653, 2024

    work page arXiv 2024

  24. [24]

    Robust speech recognition via large-scale weak supervision

    Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision. InInternational conference on machine learning, pages 28492–28518, 2023

    2023

  25. [25]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. InInternational conference on machine learning, pages 8748–8763, 2021

    2021

  26. [26]

    Mer 2024: Semi-supervised learning, noise robustness, and open- vocabulary multimodal emotion recognition

    Zheng Lian, Haiyang Sun, Licai Sun, Zhuofan Wen, Siyuan Zhang, Shun Chen, Hao Gu, Jinming Zhao, Ziyang Ma, Xie Chen, et al. Mer 2024: Semi-supervised learning, noise robustness, and open- vocabulary multimodal emotion recognition. InProceedings of the 2nd International Workshop on Multimodal and Responsible Affective Computing, pages 41–48, 2024

    2024

  27. [27]

    GPT-4o System Card

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  28. [28]

    Qwen3-Omni Technical Report

    Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, Ting He, Xinfa Zhu, et al. Qwen3-omni technical report.arXiv preprint arXiv:2509.17765, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  29. [29]

    VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

    Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms.arXiv preprint arXiv:2406.07476, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  30. [30]

    Lienet: a deep convolution neural network framework for detecting deception.IEEE transactions on cognitive and developmental systems, 14(3):971–984, 2021

    Mohan Karnati, Ayan Seal, Anis Yazidi, and Ondrej Krejcar. Lienet: a deep convolution neural network framework for detecting deception.IEEE transactions on cognitive and developmental systems, 14(3):971–984, 2021

    2021

  31. [31]

    Affakt: A hierarchical optimal transport based method for affective facial knowledge transfer in video deception detection

    Zihan Ji, Xuetao Tian, and Ye Liu. Affakt: A hierarchical optimal transport based method for affective facial knowledge transfer in video deception detection. InProceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 1336–1344, 2025

    2025

  32. [32]

    video-salmonn 2: Caption-enhanced audio-visual large language models.arXiv preprint arXiv:2506.15220, 2025

    Changli Tang, Yixuan Li, Yudong Yang, Jimin Zhuang, Guangzhi Sun, Wei Li, Zejun Ma, and Chao Zhang. video-salmonn 2: Caption-enhanced audio-visual large language models.arXiv preprint arXiv:2506.15220, 2025

    work page arXiv 2025

  33. [33]

    Mlp-mixer: An all-mlp architecture for vision.Advances in neural information processing systems, 34:24261–24272, 2021

    Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Un- terthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al. Mlp-mixer: An all-mlp architecture for vision.Advances in neural information processing systems, 34:24261–24272, 2021

    2021

  34. [34]

    Attention is all you need.Advances in neural information processing systems, 30, 2017

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural information processing systems, 30, 2017. 14 Huang et al. Appendix A Comprehensive Analysis of T4-Deception Fig. A1: Cross-cultural distribu- tion of deceptive signatures across 10 behavi...

    2017