Abstract
Protection of digital multimedia content has become an increasingly important issue for content owners and service providers. Watermarking is identified as a major means to achieve copyright protection. The algorithm proposed in this work, is to use a blind watermarking scheme based on the Discrete Wavelet Transform (DWT). Watermark components are embedded in the LL subband of the 4th DWT level of the host image by quantizing coefficients of the LL subband to improve the watermark robustness. The Genetic Algorithm (GA) is used for optimizing the quantization step size parameter, and the strength of factors. The host image used is a 512×512 gray scale image and the watermark image is a 32×32 binary logo. The proposed scheme was tested against mostly known threats and it proves to give good robustness. Also, it still gives a high quality watermarked image. MATLAB Program was used to perform the watermarking task.
Highlights
Digital watermark means embedding information into digital material in such a way that it is imperceptible to the human observer, but can be detected by computer algorithms
B.Jagadeesh et al 2010 [10] presented image watermarking scheme based on Singular Value Decomposition, Quantization and Genetic Algorithms
The coefficients in higher levels of Discrete Wavelet Transform (DWT) are more robust with respect to usual attacks such as compression, low pass filtering, rescaling, and additive noise; to improve the robustness, the watermark is embedded in the LL subband in the DWT domain by applying the 4-level wavelet decomposition
Summary
Digital watermark means embedding information into digital material in such a way that it is imperceptible to the human observer, but can be detected by computer algorithms. A digital watermark is a transparent, invisible information pattern that is inserted into a suitable component of the data source by using a specific computer algorithm [1]. This invisible watermark requires a very high level of robustness. An effective image watermarking scheme mainly includes watermark generation, watermark embedding, watermark detecting or identification, and watermark attacks. Robustness: It refers to the ability to detect the watermark, after the watermarked data has passed through certain signal processing operations, examples of common operations on images including resizing, linear and non-linear filters, lossy compression, contrast adjustment ... A watermarking technique should, at least, include the following requirements [7]: 1. Robustness: It refers to the ability to detect the watermark, after the watermarked data has passed through certain signal processing operations, examples of common operations on images including resizing, linear and non-linear filters, lossy compression, contrast adjustment ... etc
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