Video Camera Identification from Sensor Pattern Noise with a Constrained ConvNet

Derrick Timmerman, Swaroop Bennabhaktula, Enrique Alegre, George Azzopardi

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Abstract

The identification of source cameras from videos, though it is a highly relevant forensic analysis topic, has been studied much less than its counterpart that uses images. In this work we propose a method to identify the source camera of a video based on camera specific noise patterns that we extract from video frames. For the extraction of noise pattern features, we propose an extended version of a constrained convolutional layer capable of processing color inputs. Our system is designed to classify individual video frames which are in turn combined by a majority vote to identify the source camera. We evaluated this approach on the benchmark VISION data set consisting of 1539 videos from 28 different cameras. To the best of our knowledge, this is the first work that addresses the challenge of video camera identification on a device level. The experiments show that our approach is very promising, achieving up to 93.1% accuracy while being robust to the WhatsApp and YouTube compression techniques. This work is part of the EU-funded project 4NSEEK focused on forensics against child sexual abuse.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
EditorsMaria De Marsico, Gabriella Sanniti di Baja, Ana Fred
PublisherSciTePress
Pages417-425
Number of pages9
ISBN (Electronic)978-989-758-486-2
Publication statusPublished - 2021
Event10th International Conference on Pattern Recognition Applications and Methods ICPRAM -
Duration: 4-Feb-20216-Feb-2021

Conference

Conference10th International Conference on Pattern Recognition Applications and Methods ICPRAM
Period04/02/202106/02/2021

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