An Intelligent Deep Learning FrameWork for DeepFake Voice and Video Detection

Abstract

Deepfake technology uses advanced deep learning models to create realistic fake audio and video, leading to threats like fraud, misinformation, and identity theft. Traditional detection methods fail to identify such high-quality manipulations. This project presents a deep learning-based system to detect deepfake voice and video content automatically. CNN and LSTM models are used to identify spatial and temporal inconsistencies in videos. For voice detection, features like MFCC, pitch, and spectrograms are analyzed using CNN and RNN. The system accurately classifies media as real or fake, enhancing digital trust and security.

Introduction

The text focuses on the growing threat of DeepFake technology, where highly realistic fake videos and voice recordings can be created using AI. While useful in media and entertainment, these technologies can also lead to serious issues like misinformation, fraud, identity theft, and cybercrime. Traditional detection methods are no longer sufficient to identify such sophisticated manipulations.

To address this, the proposed system introduces a deep learning-based framework for detecting both fake video and audio content. It uses Convolutional Neural Networks (CNN) to analyze visual features in video frames and Long Short-Term Memory (LSTM) networks to detect temporal inconsistencies. For audio analysis, features like MFCC, pitch, and spectrograms are extracted and processed to identify unnatural voice patterns.

The system follows a multi-stage architecture: input acquisition, preprocessing, feature extraction (for both video and audio), deep learning analysis, and multi-modal fusion. By combining audio and video results, it improves detection accuracy and provides a final classification (real or fake) along with a confidence score.

Results show that the system achieves around 75% overall accuracy, with effective detection of both visual and audio anomalies, even under varied conditions.

Overall, the project presents a reliable and practical solution for identifying DeepFake content, enhancing digital security and trust in modern communication systems.

Conclusion

The proposed deep learning-based system effectively detects DeepFake voice and video content by using CNN, LSTM, and MFCC-based feature extraction. The multi-modal approach improves accuracy and reliability by combining both audio and video analysis. The system successfully classifies media as real or fake and performs well under different conditions, making it suitable for applications in cybersecurity and digital forensics. In the future, the system can be enhanced for real-time detection and deployed as a web or mobile application. Further improvements can include the use of advanced models such as Transformers and optimization techniques to reduce computational cost. Expanding the dataset and improving generalization will also help in detecting more 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, Suriyadheepan P, Suseendar R, Sudhakar , 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.