DocumentCode
3779360
Title
A blind robust image watermarking approach exploiting the DFT magnitude
Author
Mohamed Hamidi;Mohamed El Haziti;Hocine Cherifi;Driss Aboutajdine
Author_Institution
Associated Unit to the CNRST-URAC N 29, Faculty of Sciences, University of Mohammed V, BP 1014 Rabat, Morocco
fYear
2015
Firstpage
1
Lastpage
6
Abstract
Due to the current progress in Internet, digital contents (video, audio and images) are widely used. Distribution of multimedia contents is now faster and it allows for easy unauthorized reproduction of information. Digital watermarking came up while trying to solve this problem. Its main idea is to embed a watermark into a host digital content without affecting its quality. Moreover, watermarking can be used in several applications such as authentication, copy control, indexation, Copyright protection, etc. In this paper, we propose a blind robust image watermarking approach as a solution to the problem of copyright protection of digital images. The underlying concept of our method is to apply a discrete cosine transform (DCT) to the magnitude resulting from a discrete Fourier transform (DFT) applied to the original image. Then, the watermark is embedded by modifying the coefficients of the DCT using a secret key to increase security. Experimental results show the robustness of the proposed technique to a wide range of common attacks, e.g., Low-Pass Gaussian Filtering, JPEG compression, Gaussian noise, salt & pepper noise, Gaussian Smoothing and Histogram equalization. The proposed method achieves a Peak signal-to-noise-ration (PSNR) value greater than 66 (dB) and ensures a perfect watermark extraction.
Keywords
"Watermarking","Discrete cosine transforms","Robustness","Discrete Fourier transforms","Digital images","Discrete wavelet transforms","Image coding"
Publisher
ieee
Conference_Titel
Computer Systems and Applications (AICCSA), 2015 IEEE/ACS 12th International Conference of
Electronic_ISBN
2161-5330
Type
conf
DOI
10.1109/AICCSA.2015.7507124
Filename
7507124
Link To Document