 from [MIT Media Lab](https://www.media.mit.edu/)](/assets/images/featured2-547349ed043112540948e9bf7aef9913.jpeg)
In the last several years, we constantly hear that we are close to be living in a post-truth era. Every TV set speaks about fake news, confirmatione bias and more recently about deepfakes — videos processed with deep learning techniques where the face of an original person is substituted with the fake one.
If several years ago we were just laughing at the first deepfake attempts, now the technology has matured, and we have to find a way to live in this new reality.
 from [MIT Media Lab](https://www.media.mit.edu/)](/assets/images/featured2-fdbaa2ecdf7b30e6e8ab8213f0d6e47d.jpeg)
The growth of video content is unprecedented. As of 2019, more than 720 thousand hours of videos are uploaded to YouTube every day, more than 1 billion of hours of video are watched daily only on this platform. In 2021, these numbers globally might be several times higher given the huge popularity of the services like TikTok and Instagram Stories. With such rapid growth of video volume, it is obvious that we desperately need systems that would enable us to index and search video data using queries of different types.