Deepfake
Detection
Analyze image, audio, and video files with our proprietary deepfake detection solution.
Our portal is designed to recognize the latest AI-based media manipulation and synthesis
techniques, including fake human faces in images and realistic face swaps in videos.
What are Deepfakes?
Deepfakes often transform existing source content where one person is swapped for another. They also create entirely
original content where someone is represented doing or saying something they didn't do or say. Deepfakes are synthetic
media that have been digitally manipulated to replace one person's likeness convincingly with that of another. While the act
of creating fake content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence
to manipulate or generate visual and audio content that can more easily deceive. The main machine learning methods used to
create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders
or generative adversarial networks (GANs).
Sample Deepfake Videos
Deepfake Media Types
Video Deepfake
When working from a source video, a neural network-based deepfake autoencoder analyzes
the content to understand relevant attributes of the target, such as facial expressions
and body language. It then imposes these characteristics onto the original video.
This autoencoder includes an encoder, which encodes the relevant attributes; and a decoder,
which imposes these attributes onto the target video.
Image Deepfake
Deepfake images are digitally generated images or videos made with artificial intelligence. It can create high quality images of people who don't even exist. Or create videos of public figures doing or saying things they never did.
Audio Deepfake
An audio deepfake is a type of artificial intelligence used to create convincing speech sentences
that sound like specific people saying things they did not say.This technology was initially
developed for various applications to improve human life. For example, it can be used to produce
audiobooks,and also to help people who have lost their voices to get them back.Commercially, it has opened the door to several
opportunities.
Analyze image, audio, and video files with our proprietary deepfake detection solution.
Our portal is designed to recognize the latest AI-based media manipulation and synthesis
techniques, including fake human faces in images and realistic face swaps in videos.
Deepfakes often transform existing source content where one person is swapped for another. They also create entirely
original content where someone is represented doing or saying something they didn't do or say. Deepfakes are synthetic
media that have been digitally manipulated to replace one person's likeness convincingly with that of another. While the act
of creating fake content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence
to manipulate or generate visual and audio content that can more easily deceive. The main machine learning methods used to
create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders
or generative adversarial networks (GANs).
Video Deepfake
When working from a source video, a neural network-based deepfake autoencoder analyzes the content to understand relevant attributes of the target, such as facial expressions and body language. It then imposes these characteristics onto the original video. This autoencoder includes an encoder, which encodes the relevant attributes; and a decoder, which imposes these attributes onto the target video.
Image Deepfake
Deepfake images are digitally generated images or videos made with artificial intelligence. It can create high quality images of people who don't even exist. Or create videos of public figures doing or saying things they never did.
Audio Deepfake
An audio deepfake is a type of artificial intelligence used to create convincing speech sentences that sound like specific people saying things they did not say.This technology was initially developed for various applications to improve human life. For example, it can be used to produce audiobooks,and also to help people who have lost their voices to get them back.Commercially, it has opened the door to several opportunities.