Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Diram Tabaa

dblp:329/0674 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0003-0668-6050ORCID · reported

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

Security and privacy · 1 · 1 since 2021

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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Digital forensics and information hiding › digital forensics
multimedia forensics
0.612022
Video Source Characterization Using Encoding and Encapsulation Characteristics · IEEE Trans. Inf. Forensics Secur. 2022
Digital forensics and information hiding › digital forensics › multimedia forensics
source identification
0.612022
Video Source Characterization Using Encoding and Encapsulation Characteristics · IEEE Trans. Inf. Forensics Secur. 2022

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

joint metadata representation · 0.6hierarchical classification · 0.6
YearPublicationVenuePosition
2022 Video Source Characterization Using Encoding and Encapsulation Characteristics
abstract
We introduce the use of video coding settings for source identification and propose a new approach that incorporates encoding and encapsulation aspects of a video. To this end, a joint representation of the overall file metadata is developed and used in conjunction with a two-level hierarchical classification method. At the first level, our method groups videos into metaclasses considering several abstractions that represent high-level structural properties of file metadata. This is followed by a more nuanced classification of classes that comprise each metaclass. The method is evaluated on more than 20K videos obtained by combining four public video datasets. Tests show that a balanced accuracy of 91% is achieved in correctly identifying the class of a video among 119 video classes. This corresponds to an improvement of 6.5% over the conventional approach based on video file encapsulation characteristics. Analysis performed on a large, unlabeled video set also confirmed the aptness of our approach. To further demonstrate the versatility of encoding parameters, we consider attribution of partial video files where file metadata is not available. Our results show that, even in this limited setting that is intrinsic to forensic file recovery, an identification accuracy of 57% can be achieved through the use of a subset of encoding parameters estimated from coded video data.
Enes Altinisik, Husrev T. Sencar, Diram Tabaa
IEEE Trans. Inf. Forensics Secur.3