Deep Fake Video Detection Using Transfer learning

Aim:

      To enhance deep fake detection by extracting facial features using FaceNet512 and training these features with transfer learning models. Upon detecting deep fake content, the system will automatically send an email alert with the manipulated image.

Introduction:

       Deepfake videos and images have grown into a major societal concern due to their misuse in spreading misinformation, identity theft, and defamation. The rapid advancements in deep learning and AI-based video synthesis have made it increasingly difficult to differentiate between authentic and manipulated media. Traditional deepfake detection techniques primarily rely on convolutional neural networks (CNNs), but these methods often struggle to generalize across different datasets and manipulation techniques. Moreover, many existing approaches process entire video frames, leading to excessive computational costs and slower inference times. Our approach enhances detection accuracy by using FaceNet512 for precise facial feature extraction and applying transfer learning models for effective classification. By analyzing only facial embeddings, our method significantly reduces processing time while improving the system’s ability to detect deepfake artifacts. The proposed system also incorporates an automated alert mechanism, ensuring that detected deepfakes are reported to relevant stakeholders in real time. Furthermore, our approach improves generalization by training on diverse datasets, allowing the model to adapt to new and emerging deepfake techniques.

Problem Definition:

     Current deepfake detection methods face several challenges, including robustness against unseen manipulations, excessive computational requirements, and lack of real-time alert mechanisms. Many existing models rely on full-frame analysis, which increases processing time and computational overhead without necessarily improving detection accuracy. Additionally, deepfake generation techniques continue to evolve, making it difficult for conventional detection models to keep up with new forms of media manipulation. Another critical issue is the absence of automated response systems to alert users about detected deepfakes. Without a proper notification system, the impact of deepfake detection remains limited, as users may not be aware of fraudulent content in time to take action. Our proposed system addresses these challenges by focusing on facial feature extraction rather than full-frame processing, which reduces computational load while enhancing detection accuracy. Furthermore, the system integrates an automated email alert mechanism, ensuring real-time notifications whenever deepfake content is detected. This approach not only

       improves detection efficiency but also makes the system more practical for real-world applications.

Existing System:

       The existing system primarily uses EfficientNet with CNN and RNN-based architectures for detecting deepfakes. It applies autoencoders to learn feature representations and classifies videos using CNN and LSTM networks. While this approach has shown promising results, it has several limitations, including high computational requirements and difficulty in handling unseen deepfake attacks. The reliance on full-frame analysis rather than facial feature-based classification often leads to inefficiencies, particularly when processing high-resolution videos. Additionally, the existing system does not include an automated alert mechanism, making it less effective in real-world scenarios where timely notifications are crucial. Another drawback is its limited generalization capability, as models trained on specific datasets tend to struggle when tested on new manipulation techniques. Furthermore, the absence of a streamlined preprocessing pipeline results in increased latency during inference, making real-time deployment challenging. Despite achieving relatively high accuracy on seen datasets, the existing system fails to maintain consistent performance across different types of deepfake videos, highlighting the need for an improved approach.

Disadvantages of the Existing System:

• Requires extensive computational power for video-based processing, leading to inefficiencies in real-time applications.
• Struggles with generalization on unseen deepfake attacks, making it vulnerable to new forms of manipulated content.
• No automated alert system for detected deepfakes, limiting its practical usability in real-world scenarios.
• Dependency on residual image analysis for accuracy improvement, which may not always be effective against advanced deepfake techniques.
• High latency during inference due to complex preprocessing pipelines, making real-time detection challenging.
• Difficulty in adapting to evolving deepfake generation methods, reducing the long-term effectiveness of the detection system.

Proposed System:

      The proposed system introduces a more efficient and robust approach to deepfake detection by integrating FaceNet512 for feature extraction and using transfer learning models for classification. This method eliminates the need for full-frame processing, significantly reducing computational overhead while maintaining high accuracy. The core components of the system include:

1. Feature Extraction: FaceNet512 extracts high-dimensional facial embeddings, enabling more precise detection of manipulated content.
2. Transfer Learning: Pre-trained models are fine-tuned using these embeddings for deepfake classification.
3. Automated Alerts: Upon detecting deepfake content, the system sends an email containing the manipulated image.
4. Improved Generalization: The model is trained on diverse datasets to enhance its ability to detect deepfakes across various manipulation techniques.
5. Real-Time Processing: By focusing solely on facial features, the system achieves faster inference times, making it suitable for real-world applications.