journal article·2025Open access

A Comprehensive Review on File Containers-Based Image and Video Forensics

Pengpeng Yang, Chen Zhou, Dasara Shullani, Lanxi Liu, Daniele Baracchi

Computers, Materials & Continua· Volume 85· Issue 2

Abstract

Images and videos play an increasingly vital role in daily life and are widely utilized as key evidentiary sources in judicial investigations and forensic analysis. Simultaneously, advancements in image and video processing technologies have facilitated the widespread availability of powerful editing tools, such as Deepfakes, enabling anyone to easily create manipulated or fake visual content, which poses an enormous threat to social security and public trust. To verify the authenticity and integrity of images and videos, numerous approaches have been proposed, which are primarily based on content analysis and their effectiveness is susceptible to interference from various image or video post-processing operations. Recent research has highlighted the potential of file containers analysis as a promising forensic approach that offers efficient and interpretable results. However, there is still a lack of review articles on this kind of approach. In order to fill this gap, we present a comprehensive review of file containers-based image and video forensics in this paper. Specifically, we categorize the existing methods into two distinct stages, qualitative analysis and quantitative analysis. In addition, an overall framework is proposed to organize the exiting approaches. Then, the advantages and disadvantages of the schemes used across different forensic tasks are provided. Finally, we outline the trends in this research area, aiming to provide valuable insights and technical guidance for future research.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Digital Media Forensic Detection · Advanced Steganography and Watermarking Techniques · Generative Adversarial Networks and Image Synthesis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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