研究目的
To develop a secure and robust color image watermarking algorithm for copyright protection based on lifting wavelet transform.
研究成果
The proposed color image watermarking algorithm based on lifting wavelet transform demonstrates good imperceptibility with PSNR values above 40 dB and robustness against various geometric and image processing attacks, as evidenced by correlation coefficients close to 1 in many cases. It is effective for copyright protection purposes.
研究不足
The paper does not explicitly mention limitations, but potential constraints could include the reliance on specific test images, the use of a non-blind extraction method requiring the original host image, and the computational complexity of LWT and attacks.
1:Experimental Design and Method Selection:
The methodology involves using lifting wavelet transform (LWT) for decomposing images into sub-bands, embedding a watermark in the transform domain with a security key for security, and evaluating robustness against attacks. The Haar wavelet is selected as the base wavelet, and integer-to-integer LWT is used for reversibility.
2:Sample Selection and Data Sources:
Two standard test images ('Lena' and 'Baboon') are used as host images, both RGB color images with dimensions 512×512×3. The watermark is an RGB color image ('Peppers') with dimensions 256×256×
3:The watermark is an RGB color image ('Peppers') with dimensions 256×256×List of Experimental Equipment and Materials:
3.
3. List of Experimental Equipment and Materials: No specific equipment or materials are mentioned; the method is computational and uses standard test images.
4:Experimental Procedures and Operational Workflow:
The embedding process includes applying LWT to host and watermark images, selecting HHw sub-band of watermark for embedding into LL sub-band of host using a security key and random indices, and performing inverse LWT. The extraction process involves applying LWT to watermarked and original images, using the same security key to retrieve indices, extracting the watermark, and performing inverse LWT.
5:Data Analysis Methods:
Performance is evaluated using peak signal-to-noise ratio (PSNR) for imperceptibility and correlation coefficient (CC) for robustness. Attacks applied include 3×3 mean filtering, 3×3 median filtering, salt & pepper noise, rotation (45° and 90°), rescaling, cropping to 1/4 size, and JPEG compression to 25% quality.
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