-
Home page
-
Audio Modal
-
Video Modal
-
Image Modal
Inspiration
Inspiration
The increasing use of AI-generated images, videos, and audio makes it difficult to distinguish genuine content from manipulated media. We were inspired to build OriginX to help users identify potential deepfakes and improve trust in digital content.
What it does
OriginX is an AI-powered deepfake detection system that analyzes media and predicts whether it is real or fake. It aims to provide confidence scores and visual explanations, such as Grad-CAM heatmaps, to help users understand the model's predictions.
How we built it
We developed OriginX using Python and AI/deep learning techniques, integrating a user-friendly interface for media analysis. We explored Explainable AI techniques to visualize regions influencing predictions and make the results more understandable.
Challenges we ran into
Some major challenges included detecting subtle manipulations, handling different media formats, improving prediction reliability, and presenting model results in a way users can easily understand.
Accomplishments that we're proud of
We are proud to have developed an AI-based deepfake detection prototype that combines media classification with explainability features. Our project focuses not only on detecting manipulated content but also on helping users understand the predictions.
What we learned
We gained practical experience in AI, deep learning, model integration, and Explainable AI. We also learned the importance of testing, debugging, and building an interface that presents technical results clearly.
What's next for ORIGINX?
Our future plans include improving detection accuracy, expanding support for different media formats, strengthening multimodal analysis, and developing more reliable explanations. We also aim to make OriginX more accessible and robust for real-world use.
Log in or sign up for Devpost to join the conversation.