DeeptectDeepfakeDetection
Detect deepfakes in audio, images, and video. Take detection challenges, explore viral deepfakes, and stay informed and protected.
FAQ
Questions Answered
What is a Deepfake?
Deepfakes are synthetic audio, images, or videos that appear realistic but have been generated or altered by deep neural networks, commonly referred to as generative AI models. While these models have legitimate applications, their rapid improvement, growing popularity, and widespread availability also create opportunities for misuse.
- One form of deepfake involves imitating individuals without their knowledge or consent, which can enable various criminal activities.
- Deepfakes are also used to target large audiences with misinformation, contributing to an erosion of trust in digital content overall.
What kinds of Deepfakes can be reliably detected?
Our detectors identify only content that has been artificially generated or manipulated. They support audio, image, and video inputs, provided that a significant portion of the media has been fabricated by a generative AI model.
How do we detect Deepfakes?
We examine digital media content to identify fingerprints, meaning subtle and recurring patterns that appear when content is generated or altered by AI.
Just as a camera’s optical lens or sensor chip leaves tiny, characteristic traces in every photo it captures, generative models leave identifiable patterns in the output they produce.
Our detectors can analyse content far more thoroughly than a human could ever do. They extract and evaluate a large number of carefully engineered features across different representations and scales, and combine them to reach a prediction. This allows us to uncover inconsistencies and patterns that are invisible to the human eye or ear.
About
How It Started
This project emerged from university research in advanced deepfake detection. In recent years, research has made significant progress, and highly capable detection models already exist. Yet these technologies rarely reach the people and organizations who need them in everyday digital life. We are closing that gap.
We transform our research and detection models into an accessible platform that delivers practical analysis for real-world users.
We support people in navigating a media landscape where synthetic content is becoming increasingly realistic and widespread. That means not only building detection technology, but also raising awareness of emerging deepfake trends, strengthening people’s ability to recognize manipulated media through education and hands-on challenges, and enabling professional use of our detectors through APIs.
Roadmap
The Journey Ahead
What we are currently working on
- First release of our image deepfake detection
Preparing our service to reliably analyse images for signs of manipulation and make this feature available to users for the first time.
- Service stability & reliability
Ensuring the service runs smoothly, remains available, and delivers consistent results, even as usage grows.
What we plan to do next
- Audio deepfake detection
Expanding our technology to also analyse voices and audio clips, helping identify AI-generated or manipulated speech.
- Social media detection bots
Automated bots that monitor trending posts and respond when tagged. They can analyse suspicious content and reply with detection results to help inform the public.
- Professional API access
A secure interface that allows organizations and other software platforms to connect directly to our deepfake detection service and use it within their own tools and workflows.