Detecting Deceptive Behavior via Learning Relation-Aware Visual Representations

Dongliang Zhu

doi:10.1109/TIFS.2025.3586468

Detecting Deceptive Behavior via Learning Relation-Aware Visual Representations

2025-01-01

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DOI：10.1109/TIFS.2025.3586468

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With the rapid development and widespread adoption of digital media, deceptive behaviors have raised numerous ethical and security issues, making the research and advancement of deception detection technology particularly important. Most previous automated deception detection methods primarily focus on facial information in a visual context. However, from a psychological perspective, deceptive behavior extends beyond mere changes in facial expressions; it can also manifest through limb behaviors and subtle incoordination among body components. Motivated by this inconsistency, this paper attempts to model body behaviors and their relationships for deception detection. It is worth noting that some mainstream video understanding methods can roughly model head and limb information, but their holistic video input approach is easily affected by background interference. This limits their ability to focus on key body regions and subtle motion cues that reflect deception, thereby restricting detection performance. To address the above challenges, this paper proposes a Dynamic Learning Framework leveraging Body Part Relationship-Aware Modeling (DLF-BRAM). Within this framework, we segment and model the head and limb regions to reduce irrelevant background interference and enhance the accuracy of feature learning. The framework includes two main components: the Head-Limb Relationship-Aware Representation (HLRAR) module and the Dynamic Assessment Learning Strategy (DALS). The HLRAR module reveals the spatiotemporal relationship of the head, limbs, and their interactions, and learns deep feature representations for each cue, thereby highlighting the uniqueness of these cues. DALS evaluates the learning effectiveness of the three spatiotemporal relationships during training and dynamically adjusts their learning weights, preventing dominance by any single branch and promoting balanced learning. Extensive benchmark and ablation experiments demonstrate that our method outperforms most existing approaches, verifying its effectiveness.

Keywords:

Automated deception detection

human behavior understanding

deep learning

Detecting Deceptive Behavior via Learning Relation-Aware Visual Representations

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