mlsanityData to 14 Oct 2026

Authentication and integrity of smartphone videos through multimedia container structure analysis

arXiv 2402.06661 ↗First posted 5 Feb 2024cs.CR cs.LG cs.MMcited by 0

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Abstract (arXiv)

Nowadays, mobile devices have become the natural substitute for the digital camera, as they capture everyday situations easily and quickly, encouraging users to express themselves through images and videos. These videos can be shared across different platforms exposing them to any kind of intentional manipulation by criminals who are aware of the weaknesses of forensic techniques to accuse an innocent person or exonerate a guilty person in a judicial process. Commonly, manufacturers do not comply 100% with the specifications of the standards for the creation of videos. Also, videos shared on social networks, and instant messaging applications go through filtering and compression processes to reduce their size, facilitate their transfer, and optimize storage on their platforms. The omission of specifications and results of transformations carried out by the platforms embed a features pattern in the multimedia container of the videos. These patterns make it possible to distinguish the brand of the device that generated the video, social network, and instant messaging application that was used for the transfer. Research in recent years has focused on the analysis of AVI containers and tiny video datasets. This work presents a novel technique to detect possible attacks against MP4, MOV, and 3GP format videos that affect their integrity and authenticity. The method is based on the analysis of the structure of video containers generated by mobile devices and their behavior when shared through social networks, instant messaging applications, or manipulated by editing programs. The objectives of the proposal are to verify the integrity of videos, identify the source of acquisition and distinguish between original and manipulated videos.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 40 references that are in the map. First posted on arXiv 5 Feb 2024.

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  1. 2018An Analysis of the t-SNE Algorithm for Data Visualizationcited by 49

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