LEARNING DOUBLE-COMPRESSION VIDEO FINGERPRINTS LEFT FROM SOCIAL-MEDIA PLATFORMS

被引：13

作者：

Amerini, Irene ^{[1
]}

Anagnostopoulos, Aris ^{[1
]}

Maiano, Luca ^{[1
,2
]}

Celsi, Lorenzo Ricciardi ^{[1
,2
]}

机构：

[1] Sapienza Univ Rome, Rome, Italy

[2] ELIS Innovat Hub, Rome, Italy

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) | 2021年

基金：

欧盟地平线“2020”;

关键词：

Social networks; video forensics; deep learning; multitask learning; platform provenance analysis;

D O I：

10.1109/ICASSP39728.2021.9413366

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication tool. However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential. Most of the work performed so far on social media provenance has concentrated on images; in this paper, we propose a CNN architecture that analyzes video content to trace videos back to their social network of origin. The experiments demonstrate that stating platform provenance is possible for videos as well as images with very good accuracy.

引用

页码：2530 / 2534

页数：5

共 18 条

[1] Localization of JPEG double compression through multi-domain convolutional neural networks [J].