Comparison of human and machine performance for copy-move image forgery detection involving similar but genuine objects

Ye Zhu, Ramanathan Subramanian, Tian Tsong Ng, Stefan Winkler, Rama Ratnam

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

1 Citation (Scopus)

Abstract

Copy-move forgery (CMF) is considered easier to detect than general forgery mechanisms, but detecting it in the presence of multiple similar but genuine scene objects (SGOs) is non-trivial. We study the efficacy of human visual perception for copy-move image forgery detection (CMFD) involving SGOs, and compare the same with machine performance. Via an eye tracking study performed with 16 users where pairs of images (one real and the other tampered) were displayed in either parallel or serial fashion, we make the following observations: (1) Forgery detection is quicker and more accurate when images are spatially aligned and presented serially, so that the tampering is conspicuous. (2) Eye fixations focus on corresponding regions of the real and tampered images, with fewer and more localized fixations noted during serial comparison. (3) A gap is noted between CMFD performance of humans and machines, with each being more sensitive to different tampering factors. Overall, results reveal the need for systematic visual comparisons to distinguish SGOs from forged objects, as well as the promise of a human-machine collaborative framework to this end.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
EditorsRamakrishna Kappagantu, Arokiaswami Alphones , Rajnish Gupta, MIchael Ong
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1379-1383
Number of pages5
ISBN (Electronic)9781509025961
ISBN (Print)9781509025985
DOIs
Publication statusPublished - 8 Feb 2017
Externally publishedYes
Event2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
Duration: 22 Nov 201625 Nov 2016

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume0
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2016 IEEE Region 10 Conference, TENCON 2016
CountrySingapore
CitySingapore
Period22/11/1625/11/16

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