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Yuya Ito

dblp:60/10482 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2013
0000-0002-1292-3166ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Digital forensics and information hiding · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Digital forensics and information hiding › digital forensics
multimedia forensics
0.212013
Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise · IEEE Trans. Inf. Forensics Secur. 2013
Digital forensics and information hiding › digital forensics › multimedia forensics › source camera attribution
photo response non-uniformity
0.212013
Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise · IEEE Trans. Inf. Forensics Secur. 2013
Digital forensics and information hiding
sensor pattern noise
0.212013
Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise · IEEE Trans. Inf. Forensics Secur. 2013
Digital forensics and information hiding › acquisition device identification
source camera identification
0.212013
Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise · IEEE Trans. Inf. Forensics Secur. 2013

Methods — techniques the papers use, named apart from their topics

pairwise magnitude relations · 0.2clustering · 0.2
YearPublicationVenuePosition
2013 Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise
abstract
Owing to the rapid progress in digital camera technologies, a large amount of image content is distributed on the World Wide Web. Digital camera identification, which is the identification of the source camera of an input image, is becoming increasingly important for presenting evidence in a court and helping police investigations. In recent years, a digital camera identification method using the image sensor's pattern noise has received considerable attention. Photo-response non-uniformity (PRNU) noise is mainly generated by the existence of differences between the sensitivities of pixels, and it is useful as a fingerprint of a camera. However, the PRNU noise of an image is usually contaminated by random noise and scene content and affected by the image processing engine, which inhibits stable identification. In this paper, we propose a novel digital camera identification method using the pairwise magnitude relations of image sensor noise, which are robust to noise contamination. By performing experiments, we demonstrate that the proposed method can identify the source cameras of query images with high accuracy.
Yoichi Tomioka, Yuya Ito, Hitoshi Kitazawa
IEEE Trans. Inf. Forensics Secur.2
2011 Automated Song Selection System Complying with Emotional Requests
Ryosuke Yamanishi, Yuya Ito, Shohei Kato
ICEC2