ligalah

Deep fake detection

Ligalah Hezron's photo
Ligalah Hezron
·

2 min read

Detecting Deepfake Videos and Images

1. Introduction

  • Project Overview:

    Project on detecting deepfake videos and images, is aimed at addressing the growing threat of manipulated media in the digital age.

  • Motivation:

    The proliferation of deepfake technology poses serious risks, including misinformation, identity theft, and erosion of trust in media.

  • Objective:

    The goal is to develop an effective deepfake detection system using machine learning models to safeguard against the harmful effects of fake media.

2. Methodology

  • Data Collection:

    Collected diverse dataset of both real and deepfake videos and images from MesoNet: a Compact Facial Video Forgery Detection Network. In IEEE Workshop on Information Forensics and Security, WIFS. Research done in September 2018 at the National Institute of Informatics, Japan.

    Model Architecture:

    Employed state-of-the-art deep learning architectures, including Meso4 and MesoInception4, which have shown promising results in deepfake detection.

  • Training Procedure:

    The models were trained using the collected dataset with carefully selected hyperparameters, employing mean squared error as the loss function and Adam optimizer for optimization.

  • Evaluation Metrics:

    Performance was evaluated using standard metrics such as accuracy, precision, recall, and F1-score to assess the effectiveness of the models.

3. Results

  • Performance Evaluation:

    The models demonstrated strong performance on the test dataset, achieving high accuracy rates and robust detection capabilities.

  • Confusion Matrix:

    The confusion matrix visualizes the models' performance, showcasing their ability to correctly identify true positives and minimize false positives and false negatives.

  • Comparison with Baselines:

    The models outperformed baseline methods, highlighting the effectiveness of this approach in detecting deepfake media.

4. Demonstration

  • Demo Video: You'll give a demo.

5. Discussion

  • Challenges

    Model complexity, which required careful navigation and optimization.

  • Limitations

    Possibility of evasion techniques by adversaries and the need for continuous improvement to stay ahead of evolving deepfake technology.

6. Conclusion

  • Key Findings: This project highlights the effectiveness of machine learning models in detecting deepfake videos and images, offering a valuable tool for combating the spread of misinformation.

  • Implications: The implications of this research extend to safeguarding digital media integrity, preserving trust in online content, and protecting individuals from potential harm.

7. Q&A Session

  • Invite the panel and audience to ask any questions they may have.

  • Be open to discussing technical details, potential applications, and future directions for the deepfake detection system.