Editorial: Recent trends in multimedia forensics and visual content verification

Abstract

Huge amounts of multimedia content are in fact generated every day, pervading the web and popular sharing platforms such as social networks. Such data carry embedded traces due to the whole creation and sharing cycle, which can be recovered and exploited to assess the authenticity of a specific asset. This includes identifying the provenance of media data, the generation device or crafting method, as well as potential manipulation of the multimedia signal. Also, the massive introduction of artificial intelligence and of modern performing devices, together with new paradigms for content sharing and usage, have determined the need to research novel methodologies that can globally take into account all these important changes. This Research Topic gathers cutting-edge techniques for the forensic analysis and verification of media data, including solutions at the edge of signal processing, machine/ deep learning, and multimedia analysis. Research approaches to multimedia forensics have rapidly evolved in the last years, as a consequence of both technological advancements inmedia creation and distribution, andmethodological advancements in signal processing and learning. One evident aspect is the disruptive diffusion of deep learning models for addressing tasks related to audio-visual data. As a consequence of the impressive performance boost they brought in different areas, deep architectures nowadays dominate in multimedia forensics research as well. Then, forensic methodologies need to be updated with respect to the constant evolution of acquisition devices and data formats. Therefore, algorithms are also designed with the goal of efficiently analyzing high-resolution data, possibly subject to advanced in-camera processing. In addition, there is an increasing need for detection technologies that are able to identify synthetically generated visual data, in response to the impressive advancements of generative models based on Artificial intelligence (AI) such as Generative Adversarial Networks (GANs). We are glad to introduce the accepted manuscripts to this Research Topic, which are well aligned with these cutting-edge research trends and are authored by highly recognized OPEN ACCESS