Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/5391
Title: Digital Image Watermarking Techniques Using Artificial Intelligence
Authors: Zear, Aditi
Singh, Amit Kumar [Guided by]
Kumar, Pardeep [Guided by]
Keywords: Digital image watermarking
Robust watermarking
Algorithms
Artificial intelligence
Issue Date: 2016
Publisher: Jaypee University of Information Technology, Solan, H.P.
Abstract: In the digital information age, digital documents such as text, image, audio, video and 3D computer graphics models can be easily copied, manipulated and distributed over the open channel. Recently, many innovative techniques have been widely used to protect the multimedia data transmission, storage, sharing and processing of digital contents. The main objectives behind attacks can be to alter, modify, or delete the digital contents to illegally claim ownership or preventing the information transfer to intended recipients. The digital watermarking techniques offer a valuable solution to these challenges/issues. The watermarking is a data hiding technique for inserting digital information, also known as watermark, into digital documents, which can be later extracted or detected for the purposes of identification and authentication in many applications. Robustness, imperceptibility, capacity, security and computational cost are the major benchmark parameters for general watermarking system. However, there exists some trade-off between these parameters of the watermark. Therefore, some optimization techniques are required to balance these benchmark parameters. In recent years, different artificial intelligent techniques are used as an optimization technique to offer the optimal balance between visual quality of the watermarked image and the robustness of the extracted watermark. The thesis is divided into six different chapters. Chapter wise description of the thesis report is described as follows:
URI: http://ir.juit.ac.in:8080/jspui//xmlui/handle/123456789/5391
Appears in Collections:Dissertations (M.Tech.)

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