Research On Human Visual Feature Model And Its Semi-fragile Watermarking Algorithm

Digital watermarking technology has recently been a leading research subject in international information security field. It's an information hiding technology which is highly close to practical application. Through the method that embedding the mark into digital multimedia such as images, audios and videos, it can prove the ownership of content owners. Meanwhile, through examining and analyzing the watermarking, it can guarantee the facticity, integrality and reliability of the digital data. Digital watermarking has provided a valid solution to protect copyright and the authentication of multimedia.HVS (Human visual system) is a very complicated system. Although we are still not fully know all the features so far, rational using of certain visual features can solve many practical problems effectively. In order to pursue the best invisibility, many digital watermarking algorithms make use of the human visual features inevitably. How to integrate the human visual features to build human visual model is an important step to improve the digital watermarking algorithm. Many existing semi-fragile watermarking algorithms embed the mark based on human visual features, but they have bad invisibility and robustness.In order to improve the invisibility and the robustness of semi-fragile watermarking, through studying the human visual features and analyzing the popular semi-fragile watermarking algorithms, the paper puts forward a human visual features model in digital wavelet transform (DWT) domain and classifies images based on this model, and brings up the concept of the best restore probability of the pixel value which is from the idea of embedding watermark based on the characteristic of the attacks, and it has been adjusted in experiments. Meanwhile, it brings forward the quantized central limit theorem, which is to adjust the coefficients. These all make the embedded semi-fragile watermarking achieve the greatest robustness in the process of dynamic quantization. Besides, it gives a coefficient redressal algorithm that is how to round the decimal fraction of the coefficients after IDWT. This makes the obtained coefficients after DWT when fetch the mark and the modified one when embed the mark consistent with each other. Experimental results suggest that this algorithm leads up to a better invisibility of the carrier image, a better robustness to the image processing, such as JPEG compression, noise adding, filtering, and the other embedded information. And the bits of embeddable watermarking information account for one fourth of the number of pixel in the original image. Moreover, it can also ascertain the exact position for vicious attacks.