Advanced Deepfake Detection with our AI-Powered Software

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What are deepfakes?

We live in an era where anyone can swap their faces with leading actors in famous movies like the Star Wars sequels using deepfake technology. These deepfake videos or faces can even be done on your mobile phones.

Deepfakes are a type of disinformation which is manipulated information that aims to influence your opinion. Deepfake technology has become incredibly accessible and the number of applications is numerous, including real-time deepfake detection and audio-based deepfake detection. But this also makes abuse more accessible, which is why we created our deepfake detection software.

Types of Deepfakes

Rather than just one technique there are various types of deepfakes. We can regard deepfakes as a collective name for audiovisual data altered by smart algorithms instead of manual labour.

We distinguish 3 types of deepfakes: FaceSwap, StyleGANs and Deep Puppetry.

Note that deepfakes can be made of all visual data. There are for example horse StyleGANs. However, we focus on human faces.
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FaceSwaps: dataset + dataset = deepfake


The face of the donor file is copied and pasted, it mimics the emotions and motions of the source file.

StyleGANs: source + donor file = deepfake


Generated faces by a neural network that learned with a big dataset to create new unique faces.

Deep Puppetry: Source + donor file = deepfake

Deep Puppetry

The person in the source file mimics the emotions and motions of the donor file.

Why are deepfakes dangerous?

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Fake online personas

We are familiar with trolls and fake profiles spreading fake news. Adding hyperrealistic photos and videos featuring deepfakes creates susceptibility among peers for fake news spreading actors. With deepfake detection techniques and tools, we can better detect and prevent the spread of false information.
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Real-time manipulation

Are you sure the person on the other side of the screen is the one you think you're talking to? It's already possible to make deepfakes in real-time. With artificial intelligence and deepfake detection, we can protect ourselves from becoming a victim of a deepfake.
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Fake content

Edited videos cut and pasted together or placed unevenly already influence large peers. But what if a video were to be released of head figures stating things they have never said? How would you prove it is fake? Learn more about deepfake detection methods and video forensics to protect yourself from being misled by deepfakes.

How are deepfakes made?

1. Training

Before a neural network can make deepfakes, it needs to learn about the object it has to manipulate. Therefore, the neural network needs a dataset of this object. A bigger dataset generally means higher quality. At the moment, pre-trained neural networks can be found and bought online. With these products even less tech savvy people can create deepfakes.

2. Alteration

The neural network 'knows' after training how to alter certain objects. When the neural network is provided with donor footage, the motions can be copied and mimicked by the source footage. The software alters the image or video by altering the pixels within the source footage. In the alteration and adding of the pixels, artefacts can occur which  are traces that sometimes can be detected by us humans.

3. Disguising

Sometimes the deepfake neural network has a feature to fool detection software by disguising the deepfake artefacts within the image. These techniques range from quite simple to technically advanced. The full images can be down-sampled but also so-called 'blackbox'- and 'whitebox attacks' can be used. The techniques make it harder for detection software to spot the deepfakes.
Making deepfakes image

How we detect deepfakes


The DeepDetector pinpoints the pixels in the image the software used to determine whether the analysed footage is a deepfake or authentic. This analysis is called the 'activation map'. With this analysis the users of the software can understand the conclusion and evaluate the decision of the software themselves maintaining their autonomy.


The DeepDetector is trained on a dataset of deepfake and authentic images. During training it learns to distinguish authentic images or videos from deepfaked ones. As more deepfake techniques are invented, we will increase the datasets used for training the DeepDetector. This ensures the DeepDetector will always stay up-to-date with the latest advancements in deepfake generation.


The DeepDetector is a neural network. It outputs probabilities which indicate whether the input is deemed a deepfake or not. Higher percentages indicate that the software is more confident of its decision.
Deepfake Detection Dashboard

Other ways to spot deepfakes

What can you do to protect yourself from being a victim of deepfake attacks or being influenced by them?

Deepfakes are becoming higher in quality while being easier and cheaper to make. It consequently becomes harder to spot deepfakes with the naked eye. However, at this moment you can still look for several typical graphic inconsistencies (called artefacts) in deepfakes, the typical artefacts to look for are listed below.

Colour transition

In neural puppetry and faceswap deepfakes, the face is manipulated. Sometimes a hard colour transition can be seen on the edges of the area the deepfake software manipulated


Items like glasses might look convincing at first, but when looked closer, might actually contain some artefacts

Blurry background or context

StyleGAN deepfakes are good in making faces, but bad at creating context that make sense. Check the background and clothing, for example, to see if these make any sense

Unnatural looking eyes

Eyes are rather hard to deepfake due to its complexity. Check if the reflections in the eyes have the same angle. Does the person in the video blink? Are the irises of both eyes equally large?
Detecting Deepfakes

What else can you do when in doubt?

Think critically

Does the footage spark an emotional response for you? Disinformation often tends to influence your opinion and does frequently do so by sparking negative emotions regarding specific people. Do you react emotionally to the footage? Stay calm and check the information more closely. By thinking critically one could potentially question the footage on its authenticity.

Synthetic data  & deepfakes

There are several fact checking websites like Snopes and Bellingcat that you can visit for free. For faceswap and neural puppetry deepfakes a source file is needed to create the deepfake. If in doubt, you can reverse image or video search the image or video. And, of course, you can always request a demo version of our online tool, DeepDetector!

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