An Intelligent Deep Learning FrameWork for DeepFake Voice and Video Detection

Abstract

Deepfake technology leverages advanced deep learning techniques to generate highly realistic synthetic audio and video, posing significant risks such as misinformation, identity theft, and digital fraud. Conventional detection approaches are often ineffective against such sophisticated manipulations. This work proposes an intelligent deep learning framework for automatic detection of deepfake voice and video content. The system utilizes Convolutional Neural Networks (CNN) to extract spatial features and Long Short-Term Memory (LSTM) networks to capture temporal inconsistencies in video sequences. For audio analysis, features such as Mel-Frequency Cepstral Coefficients (MFCC), pitch, and spectrogram representations are processed using CNN and Recurrent Neural Networks (RNN). The proposed model effectively classifies media as authentic or manipulated, thereby enhancing trust and security in digital communication environments.

Introduction

Recent advances in deep learning have enabled the creation of highly realistic DeepFake media, which pose serious risks such as misinformation, fraud, and impersonation. Traditional detection methods struggle to identify these sophisticated manipulations. To address this, the proposed system uses a multi-modal deep learning approach that analyzes both video and audio content.

The framework combines CNNs to extract spatial features from video frames and LSTM networks to capture temporal inconsistencies. For audio, features like MFCC, pitch, and spectrograms are analyzed using neural networks to detect synthetic speech patterns. A fusion mechanism integrates both audio and video outputs to improve detection accuracy and reliability.

The system follows a structured pipeline including input acquisition, preprocessing, feature extraction (video and audio), deep learning analysis, multi-modal fusion, and final classification. It labels content as real or fake with a confidence score.

Experimental results show an overall accuracy of about 75%, with audio detection performing slightly better than video. The approach demonstrates improved robustness compared to single-modality methods, highlighting the effectiveness of combining audio and visual analysis for DeepFake detection.

Conclusion

This study presents a deep learning-based framework for detecting DeepFake voice and video content using CNN, LSTM, and MFCC-based feature extraction techniques. The integration of audio and visual analysis significantly improves detection accuracy and reliability. The system effectively distinguishes between real and manipulated media, making it applicable in domains such as digital forensics and cybersecurity. Future work can focus on real-time deployment through web or mobile platforms, integration of advanced architectures such as Transformers, and optimization techniques to reduce computational complexity. Expanding the dataset and improving model generalization will further enhance the system’s ability to detect sophisticated DeepFake content

References

[1] Nguyen, H., et al., “DeepFake detection using CNN models,” Journal of Artificial Intelligence Research, 2023. [2] Kumar, A., et al., “DeepFake video detection using CNN-LSTM architecture,” International Journal of Computer Vision and Applications, 2024. [3] Li, X., et al., “Audio DeepFake detection using MFCC features and machine learning,” IEEE Access, 2023. [4] Sharma, P., et al., “Hybrid CNN-BiLSTM model for audio DeepFake detection,” International Journal of Advanced Computing, 2025. [5] Zhang, Y., et al., “Multi-modal DeepFake detection using audio and video fusion,” Journal of Multimedia Systems, 2024. [6] Ahmed, S., et al., “Transformer-based DeepFake detection framework,” IEEE Transactions on Neural Networks and Learning Systems, 2025. [7] Rao, R., et al., “EfficientNet-based DeepFake detection approach,” International Journal of Machine Learning Research, 2023. [8] Patel, K., et al., “Real-time DeepFake detection using optimized CNN models,” Journal of Real-Time Image Processing, 2025.

Copyright

Copyright © 2026 Sathish Kumar S., Sudhakar S., Suriyadheepan P., Suseendar R., Sathya J.. 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.