Deepfake Detection System Using Deep Learning

• Author(s): Adittya Mondal ; Bivash Mazumder ; Ranganath ; Bhagyashri Wakde
• Paper ID: 1710909
• Page: 1361-1363
• Published Date: 25-09-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 3 September-2025

Abstract

Deepfakes are realistic synthetic images and videos generated by advanced generative models such as GANs and neural rendering pipelines. They present serious threats to privacy, trust, and public discourse by enabling impersonation, misinformation, and malicious content creation. This paper proposes a robust deepfake detection system that integrates spatial, temporal, and frequency-domain analyses using deep learning. The pipeline includes face detection and alignment, frame-level CNN feature extraction, frequency residual analysis, and temporal modeling with recurrent layers. An ensemble fusion strategy combines complementary cues to improve detection under compression and post-processing. Experimental evaluation on public benchmarks demonstrates strong accuracy, recall, and AUC-ROC, highlighting the system’s potential for deployment in content moderation workflows.

Keywords

Deepfake Detection, Deep Learning, CNN, GAN, FaceForensics++, Frequency Analysis, Temporal Modeling

Citations

IRE Journals:
Adittya Mondal , Bivash Mazumder , Ranganath , Bhagyashri Wakde "Deepfake Detection System Using Deep Learning" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 1361-1363

IEEE:
Adittya Mondal , Bivash Mazumder , Ranganath , Bhagyashri Wakde "Deepfake Detection System Using Deep Learning" Iconic Research And Engineering Journals, 9(3)