Deepfake Video Face Detection

Abstract

The emergence of deepfake technology, which relies on generative adversarial networks (GANs), has raised substantial concerns in the realm of digital media. This technology enables the manipulation of facial features in videos, leading to potential misuse for spreading false information, misrepresentation, and identity theft. As a result, there is a pressing need to establish robust methods for detecting deepfakes effectively. Detecting deepfake videos is particularly difficult due to their increasingly realistic appearance and the sophisticated techniques involved in their creation. This research introduces an innovative approach to deepfake detection that leverages advanced deep learning methodologies. Specifically, the study employs Convolutional Neural Networks (CNNs) in combination with Recurrent Neural Networks (RNNs), with a particular focus on Long Short-Term Memory (LSTM) networks, to enhance the identification process for deepfake content. The proposed model is trained on comprehensive datasets, including FaceForensics++ and the Deepfake Detection Challenge (DFDC). To bolster detection accuracy, the methodology includes a pre-processing pipeline that not only reduces the frame rates of video inputs but also isolates and focuses on facial regions using Haar Cascade classifiers. This dual approach of analyzing both spatial and temporal inconsistencies within video frames contributes significantly to the overall effectiveness of deepfake detection. Through rigorous testing, the proposed method has demonstrated a high level of accuracy in distinguishing between authentic and manipulated videos, showcasing its potential as a reliable solution in the ongoing fight against digital media fraud. It is crucial for researchers and practitioners in the field of video forensics and digital media security to further explore and refine such advanced detection techniques.

Introduction

Introduction:
The rise of deepfake videos, created using Generative Adversarial Networks (GANs), poses serious threats such as misinformation, fraud, and reputational damage. Despite legitimate uses in media industries, the deceptive nature of deepfakes demands reliable detection tools. Traditional detection methods that analyze frames or time sequences struggle as deepfake algorithms evolve. This study proposes a deep learning system that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis, enabling more accurate and robust deepfake identification.

Objectives:

1. Develop a deep learning model combining CNNs and LSTMs to distinguish between real and fake videos.

2. Detect inconsistencies in facial features and transitions across video frames.

3. Deploy a user-accessible web or mobile application for real-time deepfake detection.

Literature Review Highlights:

• Deep learning models (especially CNNs and RNNs) outperform traditional methods in deepfake detection.

• Transformer models improve detection by capturing long-range dependencies but lack integration with existing tools.

• Multi-view and adversarial learning enhance detection robustness but face real-time and scalability challenges.

• Techniques using multi-scale CNNs, attention mechanisms, and vision transformers improve spatial-temporal feature capture.

• New methods such as Adversarial Feature Similarity Learning (AFSL) increase resilience to adversarial attacks.

Proposed System Overview:

The proposed deepfake detection system uses a hybrid CNN-LSTM architecture:

• XceptionNet is employed for frame-level spatial analysis.

• LSTM models analyze temporal patterns for inconsistencies in motion or expressions.

• The system is deployed via a Flask web application for real-time video analysis.

Working Pipeline:

1. Preprocessing:

   • Reduces frame rate (e.g., from 60fps to 30fps).

   • Detects and crops faces using Haar cascades.

   • Applies normalization and data augmentation.

2. Feature Extraction:

   • CNN (XceptionNet) extracts spatial features like texture, lighting, and edge inconsistencies.

3. Temporal Analysis:

   • LSTM layers evaluate sequential frames to detect unnatural transitions or expression changes.

4. Classification:

   • Combined features are passed to a dense layer.

   • Softmax output gives the probability of a video being real or fake.

5. Deployment:

   • Integrated into a web UI where users upload videos and view predictions with confidence scores.

Results:

• System successfully classifies videos as real or fake based on aggregated frame-level analysis.

• Outputs include confidence scores and visual feedback.

• Example: A video with a score of 0.5006 is flagged as fake, while a score of 0.4996 is labeled real.

• Detection captures subtle manipulation artifacts often missed in manual inspection.

Future Scope:

• Optimize the system for real-time performance on streaming platforms.

• Improve inference speed without compromising accuracy.

• Expand dataset diversity and robustness to newer deepfake techniques.

Conclusion

Deepfake technology has emerged as a significant threat in the digital era, enabling the creation of highly realistic manipulated videos that can be used for misinformation, cybersecurity breaches, and digital fraud. As deepfake techniques continue to evolve, they pose serious risks to public trust and digital security. In response to this growing concern, this research proposes an advanced deepfake detection system that combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks for analyzing temporal sequences. By integrating both spatial and temporal analysis, the model effectively detects inconsistencies in video frames and unnatural motion patterns, leading to improved accuracy in identifying deepfakes. To enhance usability, the proposed system is deployed through a Flask-based web interface, allowing seamless interaction for users without technical expertise. This accessibility ensures that journalists, researchers, and the general public can efficiently verify the authenticity of video content. While the model demonstrates high accuracy in detecting various types of deepfakes, it faces challenges in identifying adversarial deepfakes designed to evade detection. Further optimizations and advancements in adversarial training will be required to enhance the system’s robustness against sophisticated manipulation techniques. Overall, this research makes a significant contribution to the field of deepfake detection by introducing a hybrid approach that effectively captures both spatial and temporal features. With ongoing improvements, this system has the potential to play a crucial role in combating the misuse of deepfake technology and preserving the integrity of digital media.

References

[1] Md Shohel Rana, Mohammad Nur Nobi, Beddhu Murali, Andrew H. Sung, Deepfake Detection: A Systematic Literature Review, 2020. [2] M. Wang, Z. Liu, H. Zhang, Video Deepfake Detection Using Transformers and Spatial-Temporal Features, 2023. [3] A. Patel, R. Sharma, K. Gupta, Features Enhanced Deepfake Detection Using Multi-View Learning and Adversarial Training, 2023. [4] S. Kim, J. Choi, Y. Jeong, DeepFake Video Detection Using Multi-Scale Residual Networks and Attention Mechanisms, 2023. [5] Yong Wang, Zhen Cui, Jian Yang, DeepFake Detection with Multi-Scale Convolution and Vision Transformer, Digital Signal Processing, Vol.120, 2023. [6] Xiaoming Li, Yibing Song, Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video, ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 19, Issue 1, February 2023 [7] Hao Wang, Jie Zhang, Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning, arXiv preprint arXiv:2403.08806, March 2024

Copyright

Copyright © 2025 Prof. Amol Gosavi, Rahul A. Joshi, Sejal D. Shingote, Roshani K. Pawar, Harshil U. Kansagra. 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.