Express Computer
Home  »  Artificial Intelligence AI  »  Scientists put machines on job to spot deepfake images, videos

Scientists put machines on job to spot deepfake images, videos

0 98

Deepfakes are becoming more authentic owing to the interaction of two computer algorithms to create perfect ‘fake’ images and videos, and humans are simply unable to gauge which is real or not.

Researchers now propose a new method called ‘frequency analysis’ that can efficiently expose fake images created by computer algorithms.

“In the era of fake news, it can be a problem if users don’t have the ability to distinguish computer-generated images from originals,” said Professor Thorsten Holz from the Chair for Systems Security at Ruhr-Universitat Bochum in Germany.

Deepfake images are generated with the help of computer models, so-called Generative Adversarial Networks (GANs).

Two algorithms work together in these networks: the first algorithm creates random images based on certain input data.

The second algorithm needs to decide whether the image is a fake or not.

If the image is found to be a fake, the second algorithm gives the first algorithm the command to revise the image – until it no longer recognises it as a fake.

In recent years, this technique has helped make deepfake images more and more authentic.

Deepfakes are video forgeries that make people appear to be saying things they never did, like the popular forged videos of Facebook CEO Zuckerberg and that of US House Speaker Nancy Pelosi that went viral last year.

To date, deepfakes have been analysed using complex statistical methods.

The Bochum group chose a different approach by converting the images into the frequency domain using the discrete “cosine transform”.

The generated image is thus expressed as the sum of many different cosine functions. Natural images consist mainly of low-frequency functions.

The analysis has shown that images generated by GANs exhibit artefacts in the high-frequency range.

For example, a typical grid structure emerges in the frequency representation of fake images.

“Our experiments showed that these artefacts do not only occur in GAN generated images. They are a structural problem of all deep learning algorithms,” explained Joel Frank.

“We assume that the artefacts described in our study will always tell us whether the image is a deepfake image created by machine learning,” Frank said, adding that frequency analysis is, therefore, an effective way to automatically recognise computer-generated images.

The team presented their work at the virtual International Conference on Machine Learning (ICML).

Get real time updates directly on you device, subscribe now.

Leave A Reply

Your email address will not be published.

LIVE Webinar

Digitize your HR practice with extensions to success factors

Join us for a virtual meeting on how organizations can use these extensions to not just provide a better experience to its’ employees, but also to significantly improve the efficiency of the HR processes
REGISTER NOW 

Stay updated with News, Trending Stories & Conferences with Express Computer
Follow us on Linkedin
India's Leading e-Governance Summit is here!!! Attend and Know more.
Register Now!
close-image
Attend Webinar & Enhance Your Organisation's Digital Experience.
Register Now
close-image
Enable A Truly Seamless & Secure Workplace.
Register Now
close-image
Attend Inida's Largest BFSI Technology Conclave!
Register Now
close-image
Know how to protect your company in digital era.
Register Now
close-image
Protect Your Critical Assets From Well-Organized Hackers
Register Now
close-image
Find Solutions to Maintain Productivity
Register Now
close-image
Live Webinar : Improve customer experience with Voice Bots
Register Now
close-image
Live Event: Technology Day- Kerala, E- Governance Champions Awards
Register Now
close-image
Virtual Conference : Learn to Automate complex Business Processes
Register Now
close-image