DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning
arXiv:2603.23916v3 Announce Type: replace-cross 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 contribut
The increasing sophistication of AI models necessitates more robust methods for identifying deceptive behavior, especially in high-stakes applications.
Reliable and culturally-aware deception detection AI is crucial for maintaining trust in various digital and security contexts, impacting forensics and national security.
The development of schema-driven AI with multicultural datasets moves beyond simple binary classification, providing verifiable evidence and better generalization in deception detection.
- · Security agencies
- · Forensic investigators
- · AI ethics researchers
- · Multimodal AI developers
- · Malicious actors
- · Systems relying on easily fooled detection methods
Improved reliability and transparency of AI-driven deception detection systems.
Increased scrutiny and regulation of AI systems used in sensitive applications due to enhanced verifiability.
The application of this schema-driven approach to other complex AI inference tasks requiring explainability and multicultural understanding.
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Read at arXiv cs.AI