AI-BASED INTERVIEW FRAUD DETECTION PLATFORM

This paper presents an AI-Based Interview Fraud Detection Platform designed to improve the reliability and transparency of online recruitment. The proposed approach integrates fa-cial verification, liveness detection, deepfake analysis, voice authenticity checking, and suspicious activity monitoring into a unified platform. By combining these techniques, the system aims to iden-tify potential interview irregularities and provide useful infor-mation to recruiters through automated alerts and structured reports. The platform is intended to support human decision-making rather than replace recruiter judgment. Future devel-opment will focus on improving detection accuracy, integrat-ing advanced deepfake detection models, expanding training datasets, and evaluating the platform using real-world inter-view scenarios.

[1]     A. Ro¨ssler et al., “FaceForensics++: Learning to De-tect Manipulated Facial Images,” in Proceedings of the IEEE/CVF International Conference on Computer Vi-sion (ICCV), 2019. [2]     Y. Li et al., “Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [3]     R. Tolosana et al., “DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection,” Information Fusion, vol. 64, pp. 131–148, 2020. [4]     X. Wang et al., “ASVspoof 2019: A Large-Scale Pub-lic Database of Synthesized, Converted and Replayed Speech,” Computer Speech & Language, 2020.