Models and methods of machine learning for fake content detection

Abstract

The article investigates the pressing issue of a fake content in the modern informational environment. Different methods for detecting and analyzing fakes, including AI usage, text analysis algorithms, visual information and linguistic features are examined. It is identified that a representative dataset with both fake and real content is necessary to achieve successful methods of fake content detection. This dataset is crucial for teaching models and algorithms for fake detection, as it provides them with the possibility to be trained on real examples and distinguish suspicious content from the genuine one. Machine learning, in particular deep learning models are one of the most perspective approaches for fake detection. These models are powerful tools based on artificial neural networks that are able to analyze different data types, including text, images or video. Ability to automatically consider linguistic and visual features, which is quite useful in fake differentiation is one of the crucial advantages of deep learning models. During training, models get a possibility to differentiate features and templates, that are typical for fake content and they are trained to differ it from the real content. This process includes text analysis for false statements, detection of photomontage on images and definition of anomalies in the video sequence. In addition, the article discusses the importance of collaboration between researchers, the development of open data sources for training models, and the constant updating of fake detection methods in response to the emergence of new technologies and methods of creating fake content. The research and development of these methods are key to ensuring the security and reliability of the information space in the digital society. In conclusion, the article emphasizes the need for innovative approaches and joint efforts to combat fake content, which can have serious consequences for society, and provides an important overview of methods and strategies for detecting and analyzing fakes in the modern information space.

Keywords

• fake content
• artificial intelligence
• machine learning
• text analysis algorithms