Generative AI & Deepfake Detection Software

Deepfakes introduce a new form of disinformation, presenting altered or fabricated content designed to influence perspectives. With the rise of Generative AI, deepfake technology has become widely accessible. However, this accessibility also raises concerns about potential abuse. This is why we developed our AI detection tool to safeguard against such abuse.

Tom Cruise deepfake gif

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.
Learn more
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.

How we detect deepfakes

How does AI deepfake detection technology work?

1 sec analysis

With our API-integration


Understandable results



Deepfakes & Generative AI: The New Digital Threat

Fake online persona icon

Digital Identities & Fake Profiles

We're familiar with trolls and fake profiles spreading fakes news. Now, they are amplified by deepfakes. With our AI detection tools, we can better detect and prevent the spread of false information
Real-time manipulation icon

Real-time AI manipulation

Are you sure the person on the other side of the screen is the one you think you're talking to? It is now possible to create deepfakes in real-time. With our AI detection tool, we help check and safeguard against d
Fake content icon

Fake content

What if a video were to be released of people saying things they have never said? How would you prove it is fake? Thus, deepfake detection and identity verification are vital for discerning truth from fiction

How are deepfakes made?

1. Training with AI Detection

To craft deepfakes, a neural network learns from numerous images or videos. With more data, it produces better deepfakes. Today, even non-techies can use pre-trained networks that can be found and bought online.

2. Alteration

Once trained, this program alters videos. It mimics movements from one video and applies them to another by adjusting pixels. Sometimes, these changes result in noticeable "artifacts."

3. Disguising

The program can hide these artifacts to deceive AI detectors. It might reduce video quality or employ advanced methods like "blackbox" attacks, making AI detection and identity verification tools crucial in spotting such deceptions.
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

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!

Check out our solutions
AI deepfake checker detecting celebrity deepfakes of elon musk in star trek