Impersonation in the Digital Age: A Comparative Review of Detection Techniques Against Deepfakes

• Author(s): Ochi Victor Chukwudi; Tochukwu Chinecherem Nnabuike
• Paper ID: 1710585
• Page: 648-651
• Published Date: 15-09-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 3 September-2025

Abstract

Deepfakes—AI?generated or manipulated audio?visual content—pose a growing risk to identity verification, trust, and security. This comparative review synthesizes state?of?the?art detection techniques across visual, audio, and multimodal pipelines, focusing on performance, generalization, robustness, and operational considerations (latency, cost, and privacy). We analyze benchmark datasets (e.g., FaceForensics++, DFDC, FakeAVCeleb, WildDeepfake, ASVspoof 2021) and evaluation metrics (AUC, EER, F1) and compare classical, deep, and hybrid approaches. We also examine adversarial pressures—including compression, unseen manipulations, and cross?domain shifts—and outline practical integration patterns for KYC/AML, exam proctoring, and access control. The review concludes with an engineering blueprint for a multimodal detection stack, emphasizing human?in?the?loop triage and risk scoring.

Keywords

Deepfakes, Impersonation Detection, Multimodal Biometrics, Audio Spoofing, Media Forensics, Identity Verification, KYC, Liveness Detection, Robustness, Generalization

Citations

IRE Journals:
Ochi Victor Chukwudi, Tochukwu Chinecherem Nnabuike "Impersonation in the Digital Age: A Comparative Review of Detection Techniques Against Deepfakes" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 648-651

IEEE:
Ochi Victor Chukwudi, Tochukwu Chinecherem Nnabuike "Impersonation in the Digital Age: A Comparative Review of Detection Techniques Against Deepfakes" Iconic Research And Engineering Journals, 9(3)