DocumentCode
3201965
Title
A new robust semi-blind image watermarking based on block classification and visual cryptography
Author
Fatahbeygi, Ali ; Akhlaghian, Fardin
Author_Institution
Dept. of Comput. Eng., Univ. of Kurdistan, Sanandaj, Iran
fYear
2015
fDate
11-12 March 2015
Firstpage
1
Lastpage
6
Abstract
In this paper a novel and robust image watermarking algorithm based on block classification and visual cryptography (VC) is presented. The proposed method inserts a watermark pattern without modifying the original host image. First the original image is decomposed into non-overlapping blocks. Then, we use canny edge detection and support vector machine (SVM) classification method to categorize these blocks into smooth and non-smooth (non-edge and edge) classes. The VC technique is used to generate two image shares. A master share that is constructed according to the block classification results and then owner share is generated by comparing master share together with binary watermark according to the (2,2) VC technique. To verify the ownership of the image, watermark can be retrieved by stacking the master share and the owner share. Experimental results show that the proposed watermarking scheme is completely imperceptible and also has high robustness against common image processing attacks.
Keywords
cryptography; edge detection; image classification; image watermarking; support vector machines; SVM classification method; VC; binary watermark; block classification; canny edge detection; image decomposition; image processing attacks; robust semiblind image watermarking; support vector machine classification method; visual cryptography; Cryptography; Detectors; Image edge detection; Robustness; Support vector machines; Visualization; Watermarking; Canny edge detector; Image blocks classification; Image watermarking; Support vector machine; Visual cryptography;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on
Conference_Location
Rasht
Print_ISBN
978-1-4799-8444-2
Type
conf
DOI
10.1109/PRIA.2015.7161650
Filename
7161650
Link To Document