Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/95860
標題: Features Classification Forest: A Novel Development that is Adaptable to Robust Blind Watermarking Techniques
作者: Chang, Chia-Sung
沈肇基
Shen, Jau-Ji
關鍵字: Association rule;digital watermarking;features classification forest;singular value decomposition
出版社: IEEE TRANSACTIONS ON IMAGE PROCESSING
Project: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
摘要: 
A novel watermarking scheme is proposed that could substantially improve current watermarking techniques. This scheme exploits the features of micro images of watermarks to build association rules and embeds the rules into a host image instead of the bit stream of the watermark, which is commonly used in digital watermarking. Next, similar micro images with the same rules are collected or even created from the host image to simulate an extracted watermark. This method, called the Features Classification Forest, can achieve blind extraction and is adaptable to any watermarking scheme using a quantization-based mechanism. Furthermore, a larger size watermark can be accepted without an adverse effect on the imperceptibility of the host image. The experiments demonstrate the successful simulation of watermarks and the application to five different watermarking schemes. One of them is slightly adjusted from a reference to especially resist JPEG compression, and the others show native advantages to resist different image processing attacks.
URI: http://hdl.handle.net/11455/95860
DOI: 10.1109/TIP.2017.2706502
Appears in Collections:資訊管理學系

Files in This Item:
File Description SizeFormat Existing users please Login
07932120.pdf期刊論文6.69 MBAdobe PDFThis file is only available in the university internal network    Request a copy
Show full item record
 

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.