The huge amount of multimedia content generated every day is 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 that 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. The massive introduction of artificial intelligence and of modern performing devices, together with new paradigms for content sharing and fruition, have determined the need to research novel methodologies that can globally take into account all these important changes.
The main goal of this Research Topic is to highlight cutting-edge techniques in the fields of multimedia forensics and digital content verification. Papers proposing solutions dealing with recently emerged challenges are particularly solicited. This includes the recognition of AI-generated synthetic data (e.g., deepfakes), as well as the development of adversarial attacks and defenses within media forensics problems. Understanding what to trust and how to assess the veracity of multimedia content information over social networks is also of great interest, both in the context of provenance/phylogeny reconstruction and of manipulation detection. The source identification analysis for recent devices, which incorporate advanced in-camera processing, also represents a highly relevant problem for media authentication.
This article collection is intended as a venue for contributions in those directions.
Topics of interest for this collection concern but are not limited to the following:
• Deep learning techniques for multimedia forensics
• Recognition of synthetic audio-visual content generated through AI and computer graphics
• Source identification for recent acquisition devices
• Adversarial attacks and defenses in the context of media forensics
• Provenance analysis and/or manipulation detection of media data shared over social networks and web platforms
• Manipulation detection through physical inconsistencies analysis
• Reverse image and video search for multimedia verification
• Visual framing and news media
• Journalistic workflows and practices for multimedia verification
The huge amount of multimedia content generated every day is 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 that 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. The massive introduction of artificial intelligence and of modern performing devices, together with new paradigms for content sharing and fruition, have determined the need to research novel methodologies that can globally take into account all these important changes.
The main goal of this Research Topic is to highlight cutting-edge techniques in the fields of multimedia forensics and digital content verification. Papers proposing solutions dealing with recently emerged challenges are particularly solicited. This includes the recognition of AI-generated synthetic data (e.g., deepfakes), as well as the development of adversarial attacks and defenses within media forensics problems. Understanding what to trust and how to assess the veracity of multimedia content information over social networks is also of great interest, both in the context of provenance/phylogeny reconstruction and of manipulation detection. The source identification analysis for recent devices, which incorporate advanced in-camera processing, also represents a highly relevant problem for media authentication.
This article collection is intended as a venue for contributions in those directions.
Topics of interest for this collection concern but are not limited to the following:
• Deep learning techniques for multimedia forensics
• Recognition of synthetic audio-visual content generated through AI and computer graphics
• Source identification for recent acquisition devices
• Adversarial attacks and defenses in the context of media forensics
• Provenance analysis and/or manipulation detection of media data shared over social networks and web platforms
• Manipulation detection through physical inconsistencies analysis
• Reverse image and video search for multimedia verification
• Visual framing and news media
• Journalistic workflows and practices for multimedia verification
