Abstract
In this paper, an image accreditation technique by embedding digital watermarks in images is proposed. The proposed method for the digital watermarking is based on the cosine transform. This is unlike most previous work, which used a random number of a sequence of bits as a watermark and where the watermark can only be detected by comparing an experimental threshold value to determine whether a sequence of random signals is the watermark. The proposed approach embeds a watermark with visual recognizable patterns, such as binary, gray, or color image in images by modifying the frequency part of the images. This thesis discusses the issues regarding data hiding and its application to multimedia digital information have also daily lives of the peoples in this security and communication, addressing both theoretical and practical aspects, and tackling both design and attack problems. Data hiding is modeled as a communication problem where the embedded data is the signal to be transmitted. Various embedding mechanisms target different robustness-capacity tradeoffs. The trade-off for different major categories of embedding mechanisms has been done. In this approach, an original image is decomposed into wavelet coefficients. Then, multi-energy watermarking scheme based on the qualified significant wavelet tree (QSWT) is used to achieve the robustness of the watermarking. Unlike other watermarking techniques that use a single casting energy, QSWT adopts adaptive casting energy in different resolutions. The performance of the proposed watermarking is robust to a variety of signal distortions, such as JPEG, image cropping, sharpening, median filtering, and incorporating attacks.These algorithms are covering applications such as annotation, tamper detection, copy/access control, fingerprinting, and ownership protection.
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© 2011 Springer-Verlag Berlin Heidelberg
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Bhattacharyya, D., Kim, Th., Lee, Gs. (2011). Watermarking Using Multiresolution Cosine Transformation: A Review. In: Kim, Th., Adeli, H., Ramos, C., Kang, BH. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2011. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27183-0_14
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DOI: https://doi.org/10.1007/978-3-642-27183-0_14
Publisher Name: Springer, Berlin, Heidelberg
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