Defeating fake food labels using watermarking and biosequence analysis

Naidu, V; Mohanty, M

Defeating fake food labels using watermarking and biosequence analysis

Naidu, V Mohanty, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019, 2019, pp. 435-441
Issue Date:: 2019-08-01

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Field	Value	Language
dc.contributor.author	Naidu, V
dc.contributor.author	Mohanty, M
dc.date	2019-08-05
dc.date.accessioned	2020-06-18T03:07:30Z
dc.date.available	2020-06-18T03:07:30Z
dc.date.issued	2019-08-01
dc.identifier.citation	Proceedings - 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019, 2019, pp. 435-441
dc.identifier.isbn	9781728127767
dc.identifier.issn	2324-898X
dc.identifier.issn	2324-9013
dc.identifier.uri	http://hdl.handle.net/10453/141507
dc.description.abstract	© 2019 IEEE. Fake food label is one of the leading ways to distribute a low quality food item as a high quality branded product. For example, under fake labels, significantly higher amount of fake Manuka honey is sold than what is actually being produced. In this paper, we propose a scheme to combat the spread of such low quality food items by identifying fake food labels. In our scheme, a watermarking is inserted to a genuine food label and biosequence analysis is used to detect this watermark. The proposed biosequence analysis is such that it can detect duplicate labels, for example a photocopy of the genuine label. The proposed method works by converting a label image into biological amino acid form (e.g., to A, C, D, G, H, etc. form) and then extracting a signature from the label (which is represented in amino acid form) using biological tools. These signatures are then matched against a query label image to find out its originality. Experiment with honey food labels (honey watermarked dataset created by us) shows that the proposed method has true positive rate of 91:67%.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019
dc.relation.ispartof	IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering
dc.relation.isbasedon	10.1109/TrustCom/BigDataSE.2019.00065
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Defeating fake food labels using watermarking and biosequence analysis
dc.type	Conference Proceeding
utslib.location.activity	Rotorua, New Zealand
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-18T03:07:15Z
pubs.finish-date	2019-08-08
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-08-05
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Fake food label is one of the leading ways to distribute a low quality food item as a high quality branded product. For example, under fake labels, significantly higher amount of fake Manuka honey is sold than what is actually being produced. In this paper, we propose a scheme to combat the spread of such low quality food items by identifying fake food labels. In our scheme, a watermarking is inserted to a genuine food label and biosequence analysis is used to detect this watermark. The proposed biosequence analysis is such that it can detect duplicate labels, for example a photocopy of the genuine label. The proposed method works by converting a label image into biological amino acid form (e.g., to A, C, D, G, H, etc. form) and then extracting a signature from the label (which is represented in amino acid form) using biological tools. These signatures are then matched against a query label image to find out its originality. Experiment with honey food labels (honey watermarked dataset created by us) shows that the proposed method has true positive rate of 91:67%.

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http://hdl.handle.net/10453/141507