A robust semi-fragile watermarking system using Pseudo-Zernike moments and dual tree complex wavelet transform for social media content authentication

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 P km and embedded onto the higher frequency subband namely, HL of the Extended author information available on the last page of the article 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...

Robust and Secure Zero-Watermark Architecture With Arnold and Phase Transformation

“…A digital watermarking method is typically evaluated based on several essential attributes, including imperceptibility, robustness, security, and payload [23], [24]. However, zero-watermarking stands apart by not requiring the embedding of data directly into the original image, ensuring that the visual quality of the image remains unaltered, in line with imperceptibility requirements.…”

Multi-image Feature Map-Based Watermarking Techniques Using Transformer

“…Digital watermarking techniques are generally processed on two domains [5]: Pixel-Based domain and Frequency-Based domain. To transform the image in the frequency domain, techniques such as Discrete Fourier Transform (DFT) [6], Quaternion Curvelet Transform (QCT) [7], Discrete Cosine Transform (DCT) [8], Hilbert Transform [9], Wavelet Transform [Discrete wavelet (DWT) [10], Stationary Wavelet (SWT) [11], Integer Wavelet Transform (IWT) [12], Complex Wavelet Transform (CWT) [13]], etc. The hybrid embedding techniques are also been attracted by the researchers to achieve authentication [14].…”