DeepFake Detective: A Streamlined Solution for Deepfake Detection & Face Swapping

Abstract

The proliferation of deepfake technology — synthetic media generated using deep learning techniques such as Generative Adversarial Networks (GANs) and Diffusion Models — poses an unprecedented threat to information integrity, digital identity, and public trust. Existing detection solutions are predominantly implemented in Python with GPU-dependent backends, rendering them inaccessible for real-time, privacy-preserving, web-based forensic analysis. This paper presents DeepFake Detective; a browser-native multi-modal forensic platform developed using React 18, TypeScript, and TensorFlow.js that performs deepfake detection entirely on the client side without transmitting user media to any remote server. The proposed system integrates twelve parallel analysis modules including: SSD MobileNet V1-based real-time face detection (face-api.js), Error Level Analysis (ELA), Fast Fourier Transform (FFT) frequency domain visualization, RGB/Edge/Luminance layer separation, EXIF metadata forensics (exifr library), Optical Character Recognition (Tesseract.js), audio waveform simulation, dominant colour distribution, and a weighted ensemble Authentication Score (0–100%). Two-Step hybrid CNN + RNN architecture is employed for frame-level spatial detection and temporal sequence analysis respectively. Experimental evaluation on the FaceForensics++ HQ benchmark demonstrates a detection accuracy of 91.4%, AUC-ROC of 94.7%, and F1-Score of 88.2%, competitive with state-of-the-art deep learning methods. The system further generates downloadable forensic PDF reports via html2canvas and jsPDF. This work contributes a scalable, GDPR-compliant, and privacy-first deepfake detection solution suitable for integration into social media platforms, journalism verification tools, and digital forensics pipelines.

Introduction

The rapid rise of artificial intelligence, particularly GANs and diffusion models, has enabled realistic deepfakes, raising concerns about misinformation, identity fraud, and privacy. Existing detection systems often require heavy server-side computation, limiting real-time usability, scalability, and privacy.

To address this, DeepFake Detective is introduced—a browser-native, client-side forensic platform built with React, TypeScript, and TensorFlow.js. It performs deepfake detection entirely in the browser, ensuring privacy and eliminating server dependency.

Literature Review:

• Early methods used physiological cues (e.g., eye-blinking) but lost effectiveness as GANs improved.
• Deep learning models, including CNNs, XceptionNet, autoencoders, and attention-based networks, improved detection accuracy and interpretability but often struggle with generalization on real-world data.
• Temporal and frequency-domain analyses enhance robustness, while lightweight browser-deployable models enable privacy-preserving edge detection.
• Research gaps include lack of fully browser-based systems, limited multi-modal integration, poor explainability, weak real-world generalization, and missing OCR/EXIF analysis.

System Architecture:

• Four-layer browser-native pipeline: Input (media upload/capture), Pre-processing (state management, file routing), Analysis (12 parallel modules), Output (weighted Authentication Score + forensic PDF).
• All analysis occurs client-side using WebGL-accelerated TensorFlow.js, preserving privacy.

Analysis Modules:

• Spatial (CNN), temporal (LSTM), frequency (FFT), compression (ELA), metadata (EXIF), and text analysis (OCR).
• Audio analysis detects synthetic speech artifacts.
• Outputs include a comprehensive forensic PDF with module-level scores for legal, academic, or journalistic use.

Innovation: Combines multi-modal detection in a fully browser-based, privacy-preserving platform with real-time usability and explainable outputs.

Conclusion

DeepFake Detective is a browser-native, multi-modal forensic platform for deepfake detection and face-swap analysis, built using React and TypeScript with TensorFlow.js-based client-side AI inference. It integrates twelve parallel analysis modules into a unified weighted authentication score while ensuring real-time performance and complete privacy (no server-side data transfer). The system achieves strong results on the FaceForensics++ HQ dataset (91.4% accuracy, 94.7% AUC-ROC, 88.2% F1-score) and combines hybrid CNN–RNN architecture with practical tools such as face detection, metadata analysis, OCR, and automated forensic report generation. Overall, it demonstrates that effective, privacy-preserving deepfake detection can be implemented directly in the browser, with future work focused on advanced models, WebAssembly-based forensics, and video/audio analysis for production-ready deployment.

References

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Copyright

Copyright © 2026 Payal Bodawala, Ashka Bhalodia, Kinal Patel, Bhumi Torani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.