Detecting Deepfake Faces with CNN and LSTM

Abstract

In recent years, the advent of deepfake technology has posed significant challenges to the veracity of digital content. Originally emerging as an offshoot of advancements in generative modelling, deep fakes have evolved into sophisticated tools capable of creating hyper-realistic yet entirely synthetic facial content in videos and images. These manipulations pose serious threats across various sectors including journalism, law enforcement, politics, and social media by enabling the spread of misinformation, identity fraud, and reputational damage. To address these growing concerns, this investigation proposes an integrated deepfake detection system utilizing Convolutional Neural Networks. Convolutional Neural Networks (CNNs) are employed for the extraction of spatial features across individual frames, enabling the identification of discrepancies such as artificial textures or visual anomalies. LSTMs complement this by modeling temporal dependencies across frame sequences to detect anomalies in facial movements and expressions. The combined framework enables the system to assess both static and dynamic patterns typical of deepfake manipulations. The model has undergone training and testing on a comprehensive dataset containing authentic and manipulated media. demonstrating high detection accuracy. Experimental evaluation reveals that the CNN-LSTM hybrid outperforms traditional static analysis models in identifying complex temporal inconsistencies, making it highly effective for video-based deepfake detection. Visualization modules and a user-friendly interface further support real-time use cases, enhancing interpretability and deployment potential in real-world scenarios.

Introduction

The emergence of deepfake technology, powered by deep learning methods such as GANs and autoencoders, enables the creation of highly realistic manipulated media. While deepfakes have useful applications in entertainment and education, their misuse raises serious concerns, such as fake news, fraud, and privacy violations. The ease of access to powerful tools and datasets has made it possible even for non-experts to generate deceptive content.

Problem Statement:

Traditional deepfake detection systems are often limited, especially those that analyze static images or individual video frames. These systems fail to capture temporal inconsistencies like unnatural blinking, inconsistent facial movements, or abrupt transitions across frames.

Proposed Solution:

This study proposes a hybrid deepfake detection framework combining:

• CNNs (Convolutional Neural Networks) to extract spatial (frame-level) features and detect visual anomalies.

• LSTMs (Long Short-Term Memory networks) to model temporal (time-based) patterns and detect motion inconsistencies.

This combination enables more robust and holistic detection of forged video content.

Methodology:

1. Datasets Used:

   • FaceForensics++

   • DeepFake Detection Challenge (DFDC)

   • Celeb-DF (v2)

2. Preprocessing:

   • Frame extraction, face detection using MTCNN, normalization, resizing (e.g., 224×224), and labeling.

3. Model Architecture:

   • CNN extracts visual features from individual frames.

   • LSTM processes sequences of these features to capture time-based anomalies.

   • Trained end-to-end using binary cross-entropy loss and Adam optimizer.

4. Training Details:

   • Dataset split: 70% training, 15% validation, 15% testing

   • Data augmentation used to improve generalization.

Mathematical Formulations:

• CNN operation: Convolution and pooling to extract spatial features.

• LSTM cell: Handles sequence learning with gates for input, output, and forget mechanisms.

• Loss Function: Binary cross-entropy to optimize classification accuracy.

Evaluation Metrics:

• Accuracy

• Precision

• Recall

• F1-score

• AUC-ROC

These metrics help validate the model's ability to detect deepfakes reliably across real-world scenarios.

System Integration:

• A real-time detection pipeline is implemented using:

  • Frontend: Web interface for video uploads

  • Backend: Flask or Django API

  • Database: SQLite/MySQL

  • Deployment: Docker container on AWS/GCP for scalability

Literature Survey Highlights:

• MesoNet introduced shallow CNNs for fast detection but lacked depth.

• Hybrid CNN-RNN models (e.g., Sabir et al., Guera & Delp) outperformed static models by capturing temporal patterns.

• Other approaches included:

  • Face X-ray for detecting boundary blending

  • Blink detection for behavioral inconsistency

  • Explainable AI tools like LIME and SHAP for transparency

Conclusion

The advanced deepfake detection framework using a hybrid CNN-LSTM architecture represents a notable progress in multimedia forensics and authenticating AI-generated content. This study provides a technically strong, scalable, and efficient solution by tackling the urgent issue of detecting increasingly sophisticated deepfake videos.The system was designed to extract spatial characteristics using Convolutional Neural Networks (CNNs) and capture temporal relationships using Long Short-Term Memory (LSTM) networks. This enables the system to detect both visual patterns and frame-to-frame inconsistencies characteristic of synthetic alterations. The evaluation results demonstrated that the CNN-LSTM model achieved superior performance, with accuracy exceeding 93%, precision and recall both above 91%, and an AUC-ROC greater than 0.95. These metrics validate the robustness, reliability, and predictive strength of the model in classifying deepfake videos. Furthermore, the integration of explainability elements such as feature visualizations and confidence scoring enhances the framework’s transparency, that is crucial for establishing user confidence and enabling human-involved decision-making. However, certain limitations remain and open avenues for future enhancement. The current model performance is influenced by dataset diversity and video compression artifacts.Future efforts could center on broadening the training dataset by including more demanding deepfake samples., including those generated by newer GAN variants and low-resolution manipulations. Additionally, the adoption of transformer-based temporal models and integration with blockchain-based traceability for video source verification can further elevate the system’s credibility and deployment scope. Real-time detection from live video streams and on-device inference optimizations also represent promising areas for extending the current architecture. In conclusion, the research lays a solid foundation for practical, AI-driven deepfake detection systems provide a significant contribution to the expanding literature on ethical and secure AI media authentication.

References

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Copyright

Copyright © 2025 Mrs. Subhashree D C, Ms. Nikitha M. 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.