In [16], the watermark detection is based on the correlation between the 2D STFT of the watermarked image and that of the watermark image for each image pixel.
Figure 14 (a)–(d) The watermarked Lena, Baboon, Peppers and Crowd images with 256 × 256 watermark image as shown in Figure 11(b) after JPEG compression with quality factor 40%, 30 %, 20 and 10%and10%ectively.
(i)–(l) The watermarked Lena, Baboon, Peppers and Crowd images with 256 × 256 watermark image as shown in Figure 11 d) after JPEG compression with quality factor 40%, 30 %, 20 and 10%and10%ectively.
Figure 16 (a)–(d) The watermarked Lena, Baboon, Peppers and Crowd images with 256 × 256 watermark image after wavelet compression with quality factor 0. 4 bpp, 0.8 bpp, 1.5 bpp and 3.5 bpp, respectively.
In the extraction procedure, the watermark can be extracted from the watermarked image without the requirement of the original host image or the original watermark image.
Firstly, the binary watermark image is encrypted by Arnold transform, and the RHFMs of the host image are computed.
The splitting of the watermark image into two parts, respectively, for spatial and frequency insertion relies on the user's preference and data importance.
The reason for incorporating SC in the proposed system is to encode the watermark image before embedding it in the host image.
The algorithm first processes a meaningful gray watermark image, then embeds it as a watermark signal into the block Discrete Cosine Transform (DCT) component.
The watermark image can be deciphered by the CFDH setup, the reconstructed image shows good quality and the error is almost closed to zeros.
Its application to watermarking has been demonstrated by a simulation procedure, in which the watermark image to be hidden is encoded into the phase of the host image.
