Camera Model Identification Using Convolutional Neural Networks

被引：0

作者：

Kuzin, Artur ^{[1
]}

Fattakhov, Artur ^{[2
]}

Kibardin, Ilya ^{[2
]}

Iglovikov, Vladimir I. ^{[3
]}

Dautov, Ruslan ^{[4
]}

机构：

[1] Moscow Inst Phys & Technol, Dbrain ODS Ai, Moscow, Russia

[2] Moscow Inst Phys & Technol, Dept Innovat & High Technol, Moscow, Russia

[3] Lyft Inc, Autonomous Vehicle Div, Level 5, San Francisco, CA USA

[4] Shenzhen Univ, Big Data Inst, Shenzhen, Peoples R China

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2018年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Source camera identification is the process of determining which camera or model has been used to capture an image. In recent years, there has been a rapid growth of research interest in the domain of forensics. In the current work, we describe our Deep Learning approach to the camera detection task of 10 cameras as a part of the Camera Model Identification Challenge hosted by Kaggle.com where our team finished 2nd out of 582 teams with the accuracy on the unseen data of 98%. Augmentations that allowed a stay robust against transformations. A number of experiments are carried out on datasets collected by organizers and scraped from the web.

引用

页码：3107 / 3110

页数：4

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