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A robust semi-fragile watermarking system using Pseudo-Zernike moments and dual tree complex wavelet transform for social media content authentication.
Agilandeeswari, L; Prabukumar, M; Alenizi, Farhan A.
  • Agilandeeswari L; Vellore, 632014 India School of Information Technology and Engineering (SITE), Vellore Institute of Technology.
  • Prabukumar M; Vellore, 632014 India School of Information Technology and Engineering (SITE), Vellore Institute of Technology.
  • Alenizi FA; Al-Kharj, 16278 Saudi Arabia Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University.
Multimed Tools Appl ; : 1-53, 2023 Apr 13.
Article in English | MEDLINE | ID: covidwho-2290666
ABSTRACT
With the growing use of mobile devices and Online Social Networks (OSNs), sharing digital content, especially digital images is extremely high as well as popular. This made us convenient to handle the ongoing COVID-19 crisis which has brought about years of change in the sharing of digital content online. On the other hand, the digital image processing tools which are powerful enough to make the perfect image duplication compromises the privacy of the transmitted digital content. Therefore, content authentication, proof of ownership, and integrity of digital images are considered crucial problems in the world of digital that can be accomplished by employing a digital watermarking technique. On contrary, watermarking issues are to triumph trade-offs among imperceptibility, robustness, and payload. However, most existing systems are unable to handle the problem of tamper detection and recovery in case of intentional and unintentional attacks concerning these trade-offs. Also, the existing system fails to withstand the geometrical attacks. To resolve the above shortcomings, this proposed work offers a new multi-biometric based semi-fragile watermarking system using Dual-Tree Complex Wavelet Transform (DTCWT) and pseudo-Zernike moments (PZM) for content authentication of social media data. In this research work, the DTCWT-based coefficients are used for achieving maximum embedding capacity. The Rotation and noise invariance properties of Pseudo Zernike moments make the system attain the highest level of robustness when compared to conventional watermarking systems. To achieve authentication and proof of identity, the watermarks of about four numbers are used for embedding as a replacement for a single watermark image in traditional systems. Among four watermarks, three are the biometric images namely Logo or unique image of the user, fingerprint biometric of the owner, and the metadata of the original media to be transmitted. In addition, to achieve the tamper localization property, the Pseudo Zernike moments of the original cover image are obtained as a feature vector and also embedded as a watermark. To attain a better level of security, each watermark is converted into Zernike moments, Arnold scrambled image, and SHA outputs respectively. Then, to sustain the trade-off among the watermarking parameters, the optimal embedding location is determined. Moreover, the watermarked image is also signed by the owner's other biometric namely digital signature, and converted into Public key matrix Pkm and embedded onto the higher frequency subband namely, HL of the 1-level DWT. The proposed system also accomplishes a multi-level authentication, among that the first level is attained by the decryption of the extracted multiple watermark images with the help of the appropriate decryption mechanism which is followed by the comparison of the authentication key which is extracted using the key which is regenerated at the receiver's end. The simulation outcomes evident that the proposed system shows superior performance towards content authentication, to most remarkable intentional and unintentional attacks among the existing watermarking systems.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Multimed Tools Appl Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Multimed Tools Appl Year: 2023 Document Type: Article