4.6 Article

Dual Watermarking for Color Images: A New Image Copyright Protection Model based on the Fusion of Successive and Segmented Watermarking

Journal

MULTIMEDIA TOOLS AND APPLICATIONS
Volume 79, Issue 9-10, Pages 6503-6530

Publisher

SPRINGER
DOI: 10.1007/s11042-019-08290-w

Keywords

Dual Watermarking; Information Fusion; Image Copyright Protection; Optimization; Wavelet-based Feature Extraction; Walsh Encryption

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Watermarking has been suggested as a generic technique to solve various problems associated with topics in the areas of digital rights management and multimedia security. Most of the early methods were based on single watermark embedding, but there are great limitations when single watermark embedding algorithms are tried into practical applications. The multiple watermarks are intended to convey different information at the same time. Current approaches rely on adding many watermarks in different bands or channels by means of scaling factor and embedding locations that are mainly defined by experts. This brought many challenges in achieving equilibrium between security, robustness, and quality. The aim of this work is to introduce an intelligent dual watermarking model for colour images that ensure image security for copyright protection (dual watermarking for single purpose). To optimize the dual watermarking requirements, the proposed model that employs both successive (re-) and segmented watermarking techniques is to be implemented with a multi-purpose evolutionary algorithm. Genetic algorithm is adopted to determine the embedding locations and scaling factors for different watermarks according to the features of each host image to balance between imperceptibility and robustness. The wavelet transform is utilized for salient features extraction because of its excellent space-frequency localization of salient image features. In addition, the suggested model encrypts the watermarks with the aid of Walsh transform; so that it is difficult to handle the watermarks even after being extracted by the attackers. Experimental results show that the proposed model is more robust against common image manipulation attacks in terms of PSNR and NCC.

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