Combating Digitally Altered Images: Deepfake Detection

In today's digital age, the rise of deepfake technology poses significant threats to credibility, privacy, and security. This article delves into our Deepfake Detection Project, a robust solution designed to combat the misuse of AI-generated content. Our team, comprising Microsoft Learn Student Ambassadors - Saksham Kumar and Rhythm Narang both from India embarked on this journey to create a tool that helps users verify the authenticity of digital images.

Project Overview

The Deepfake Detection Project aims to provide a reliable tool for detecting and classifying images as either real or deepfake. Our primary goal is to reduce the spread of misinformation, protect individuals from identity theft, and prevent the malicious use of AI technologies. By implementing this tool, we hope to safeguard digital integrity and maintain trust in online interactions and media.

How did we come up with the idea?

We were going through an article about how deepfakes are damaging credibility, spreading misinformation, and public news/records being manipulated to create hatred and fear amongst the public. As Microsoft Learn Student Ambassadors, we saw an opportunity to address this issue by developing a deepfake detector. Initially, we had a rough idea, but the AI Projects platform provided us with the resources and support needed to bring our vision to life.

What is Deepfake? What is the issue?

As per-Wikipedia, Deepfakes (a combination of "deep learning" and "fake") are images, videos, or audio files that are altered or completely generated using artificial intelligence tools to depict real or non-existent people in a convincing yet deceptive manner. Deepfake technology enables the creation of highly realistic but fabricated content, which can easily mislead audiences.

Technical Details

Our Deepfake Detection Project provides a robust tool to detect and classify images as either real or deepfake, helping users verify the authenticity of digital content before sharing or trusting it. By implementing this tool, we aim to reduce the spread of misinformation, protect individuals from identity theft, and prevent the misuse of AI technologies for malicious purposes.

Model Architecture - Deepfake Detection Model

We applied transfer learning on the Open Forensics Deepfake Detection Dataset, using approximately 1.9 million images divided into training, validation, and testing sets. The model was trained on Azure ML Studio notebooks with a compute VM of 16 cores and 128 GB RAM for six hours, achieving an accuracy of 97% on the testing set. Additionally, we developed a Django-based web application that retrieves images from users, sends them to the model via an API call, and displays the results.

The trained model is deployed on the Hugging Face Hub and is available for public use (please refer to the end of this article!). Here's a video showing more about this project

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Results and Outcomes

The Deepfake Detection Project has achieved significant results, including a 97% accuracy rate on the testing set. The web application allows users to upload images and receive real-time feedback on their authenticity. This tool has the potential to prevent the spread of false information, protect individuals from identity theft, and promote the responsible use of AI technologies.

Lessons Learned

This project pushed us to explore the necessity of responsible AI practices, and also how false actors might use technology in a way to harm not just financially or socially, but also to make us reflect on how and to whom we share our data.

On the bright side, it opens doors for opportunities for people to work on combating this issue with the ethical usage of technology, ultimately leading to a future where technology is not only innovative but also aligned with humanity's core values.

Future Development

We plan to enhance the Deepfake Detection Project by incorporating additional features, such as video analysis and real-time detection capabilities. We also aim to expand the dataset and improve the model's accuracy by implementing newer, better algorithms. Our commitment to continuous improvement ensures that this tool will remain relevant and effective in combating deepfake threats.

Conclusion

AI technology has progressed to the point were creating highly realistic deepfakes is accessible to nearly anyone. This ease of use, while beneficial in some creative contexts, has serious implications for personal privacy, media integrity, politics, finance, and security. Deepfakes are becoming harder to detect with the naked eye, which means they can easily be used to mislead the public. Fake videos of politicians saying inflammatory things or fabricated news reports can rapidly spread misinformation and impact societal stability.

Our Deepfake Detection Project aims to address these issues by giving individuals and organizations a way to detect deepfakes before they cause harm. In this way, our project doesn’t just provide a technical solution—it supports digital integrity and security, helping to maintain trust in online interactions and media. By advocating awareness and collaboration, we can ensure that artificial intelligence serves as a tool for empowerment rather than exploitation, promoting trust, equity, and progress in society.

Call to Action

We encourage readers to:

• Engage with our Deepfake Detection project and provide feedback - Deepfake Detection Application | GitHub
• Check out the Deepfake Model on HuggingFace - https://huggingface.co/sakshamkr1/deepfake_vit
• Check out the Generative AI for Beginners curriculum 
• Connect with us on LinkedIn to stay updated on our progress and future developments!
• Join the Microsoft Learn Student Ambassadors program