Artificial intelligence could eventually help to award scores to the tens of thousands of papers submitted to the Research Excellence Framework by UK universities.Credit: Yuichiro Chino/Getty Researchers tasked with examining whether artificial intelligence (AI) technology could assist in the peer review of journal articles submitted to the United Kingdom's Research Excellence Framework (REF) say the system is not yet accurate enough to aid human assessment, and recommend further testing in a large-scale pilot scheme. The team's findings, published on 12 December, show that the AI system generated identical scores to human peer reviewers up to 72% of the time. When averaged out over the multiple submissions made by some institutions across a broad range of the 34 subject-based'units of assessment' that make up the REF, "the correlation between the human score and the AI score was very high", says data scientist Mike Thelwall at the University of Wolverhampton, UK, who is a co-author of the report. In its current form, however, the tool is most useful when assessing research output from institutions that submit a lot of articles to the REF, Thelwall says. It is less useful for smaller universities that submit only a handful of articles.

The fast-spreading information over the internet is essential to support the rapid supply of numerous public utility services and entertainment to users. Social networks and online media paved the way for modern, timely-communication-fashion and convenient access to all types of information. However, it also provides new chances for ill use of the massive amount of available data, such as spreading fake content to manipulate public opinion. Detection of counterfeit content has raised attention in the last few years for the advances in deepfake generation. The rapid growth of machine learning techniques, particularly deep learning, can predict fake content in several application domains, including fake image and video manipulation. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works and future directions towards the issues and shortcomings still unsolved on deepfake detection.