TRUTH DECAYTechnology Can Detect Fake News in Videos

Published 29 June 2022

Social media represent a major channel for the spreading of fake news and disinformation. This situation has been made worse with recent advances in photo and video editing and artificial intelligence tools, which make it easy to tamper with audiovisual files, for example with so-called deepfakes, which combine and superimpose images, audio and video clips to create montages that look like real footage.

Social media represent a major channel for the spreading of fake news and disinformation. This situation has been made worse with recent advances in photo and video editing and artificial intelligence tools, which make it easy to tamper with audiovisual files, for example with so-called deepfakes, which combine and superimpose images, audio and video clips to create montages that look like real footage. Researchers from the K-riptography and Information Security for Open Networks (KISON) and the Communication Networks & Social Change (CNSC) groups of the Internet Interdisciplinary Institute (IN3) at the Universitat Oberta de Catalunya (UOC) have launched a new transdisciplinary project to develop innovative technology that, using artificial intelligence and data concealment techniques, should help users to automatically differentiate between original and adulterated multimedia content, thus contributing to minimizing the reposting of fake news. DISSIMILAR is an international initiative headed by the UOC including researchers from the Warsaw University of Technology (Poland) and Okayama University (Japan).  

The project has two objectives: firstly, to provide content creators with tools to watermark their creations, thus making any modification easily detectable; and secondly, to offer social media users tools based on latest-generation signal processing and machine learning methods to detect fake digital content,” explained Professor David Megías, KISON lead researcher and director of the IN3. Furthermore, DISSIMILAR aims to include “the cultural dimension and the viewpoint of the end user throughout the entire project”, from the designing of the tools to the study of usability in the different stages.

The Danger of Biases 
Currently, there are basically two types of tools to detect fake news. Firstly, there are automatic ones based on machine learning, of which (currently) only a few prototypes are in existence. And, secondly, there are the fake news detection platforms featuring human involvement, as is the case with Facebook and Twitter, which require the participation of people to ascertain whether specific content is genuine or fake. According to David Megías, this centralized solution could be affected by “different biases” and encourage censorship. “We believe that an objective assessment based on technological tools might be a better option, provided that users have the last word on deciding, on the basis of a pre-evaluation, whether they can trust certain content or not,” he explained. 

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