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Common Gabor Features for Image Watermarking Identification

Applied sciences, 2021-09, Vol.11 (18), p.8308 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app11188308

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  • Title:
    Common Gabor Features for Image Watermarking Identification
  • Author: Ahmed, Ismail Taha ; Hammad, Baraa Tareq ; Jamil, Norziana
  • Subjects: Algorithms ; Classification ; Classifiers ; Digital imaging ; Digital watermarks ; Discriminant analysis ; discriminant analysis (DA) classifier ; Embedding ; Gabor feature ; Localization ; Multimedia ; Random_forest classifier ; Spread spectrum ; Watermarking ; watermarking identification
  • Is Part Of: Applied sciences, 2021-09, Vol.11 (18), p.8308
  • Description: Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3417
    EISSN: 2076-3417
    DOI: 10.3390/app11188308
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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