Human visual system models in Digital Watermarking

Author(s):  
Hirak Mazumdar ◽  
Piyush Anand ◽  
Saraswati Jee Soni ◽  
Mridul Joshi ◽  
Kumar Rajeev ◽  
...  
Author(s):  
Hsien-Chu Wu ◽  
Hei-Chuan Lin

In recent years, services on the Internet have greatly improved and are more reliable than before. However, the easy downloads and duplications on the Internet have created a rush of illicit reproductions. Undoubtedly, the rights of ownership are violated and vulnerable to the predators that stalk the Internet. Therefore, protection against these illegal acts has become a mind-boggling issue. Previously, artists and publishers painstakingly signed or marked their products to prevent illegal use. However with the invention of digital products, protecting rightful ownership has become difficult. Currently, there are two schemes to protect data on the Internet. The first scheme is the traditional cryptography where the important data or secret is to be encrypted by a special process before being transmitted on the Internet. This scheme requires much computational process and time to encrypt or decrypt. On the other hand, the second scheme is steganography where the important message or secret is hidden in the digital media. The hidden data is not perceptible by the human visual system (HVS). The digital watermarking technique is an application of steganography (Chang, Huang, & Chen, 2000; Chen, Chang, & Huang 2001). In order to safeguard copyrights and rightful ownerships, a representative logo or watermark could be hidden in the image or media that is to be protected. The hidden data can be recovered and used as proof of rightful ownership. The watermarking schemes can be grouped into three kinds, largely, dependent on its application. They use the fragile watermark, semi-fragile watermark, and robust watermark, respectively (Fabien, Ross, & Markus, 1999). Fragile watermarks are easily corrupted when the watermarked image is compressed or tampered with. Semi-fragile watermarks can sustain attacks from normal image processing, but are not robust against malicious tampering. Fragile and semi-fragile watermarks are restricted in its use for image authentication and integrity attestation (Fridrich,2002; Fridrich, Memon, & Goljan, 2000). For the robust watermarking, it is always applied in ownership verification and copyright protection (Fridrich, Baldoza, & Simard, 1998; Huang, Wang, & Pan, 2002; Lu, Xu, & Sun, 2005; Solanki, Jacobsoen, Madhow, Manjunath, & Chandrasekaran, 2004). Some basic conditions must be followed: (1) Invisibility: the watermarked image must look similar to its original and any difference invisible to the human visual system. (2) Undetectable: the watermark embedded in the image must not be easily detectable by computing processes or statistical methods. (3) Safety: watermark is encrypted and if accessed by a hacker; cannot be removed or tampered with. (4) Robustness: the watermark is able to withstand normal and/or illegal manipulations, such as compression, blurring, sharpening, cropping, rotations and more. The retrieved watermark is perceptible even after these processes. (5) Independence: the watermark can be retrieved without the original image. Last but not the least, (6) Efficiency: the watermarked image should not require large storage and must also allow for a comparable-sized watermark to be hidden in the media. The proposed method is a VQ-based watermark technique that depends on the structure of a tree growth for grouping the codebook. The scheme is robust. That is, the watermark is irremovable and also can withstand normal compression process, tampering by compression or other malicious attacks. After these attacks, the watermark must be recovered with comparable perceptibility and useful in providing proof of rightful ownerships.


2013 ◽  
Vol 798-799 ◽  
pp. 785-789
Author(s):  
Na Na Zhang ◽  
Jia Fa Mao ◽  
Jing Yin ◽  
Xiao Fang Yang

This paper proposes the estimation method for the maximum payload on spatial domain, concentrates on digital watermarking payload in the spatial domain image, on the constraint of perceptual invisibility research, the influence under the factors in Human Visual System. The maximum payload is influenced by the factors which include the size of image, the brightness masking, contrast masking and texture masking of the image. with such as noise visibility function visual model, gets the just noticeable different value to calculate the payload of the image, finally we get the watermarking payload, test and verify it with Matlab simulation experiments.


2014 ◽  
Vol 687-691 ◽  
pp. 3992-3995
Author(s):  
Xiao Qiang Yang

Digital watermarking is a new information security technology, and it uses the information to protect the security of multimedia data hiding technique. Digital watermarking in wavelet domain can make effective use of the human visual system characteristics, and can be compatible with the international compression standard, and the embedding watermark signal energy can be distributed to all of the pixel space. Based on the characteristic of multi-resolution wavelet decomposition and human visual system model matching, digital watermarking algorithm based on wavelet transform is proposed in this paper. The algorithm for tamper proof is designed by quantifying the significant wavelet coefficients to embed watermark sequence. Preprocessing and quantifying the image of this algorithm are studied, which resolves the rounding error and overflow problem brought by the watermarked image pixel values of wavelet transform. Through various attack test and analysis, the experimentation shows that it has strong robustness, can resist many common image attacks, and has strong practicability.


2011 ◽  
Vol 271-273 ◽  
pp. 1339-1342
Author(s):  
Yu Hua Zhu

A chaotic digital watermarking scheme based on logistic mapping is tested by experiments. Being of human visual system(HVS) masking properties, the watermarking adapt itself into the image, which ensure the watermark is invisible. Experimental results show that the proposed algorithm has good robustness for the common signal distortion (adding salt & pepper noise, median filtering, etc.) and geometric distortion (lossy compression, shear, etc).


1998 ◽  
Vol 21 (4) ◽  
pp. 467-468
Author(s):  
David R. Andresen ◽  
Chad J. Marsolek

The human visual system is capable of learning both abstract and specific mappings to underlie shape recognition. How could dissimilar shapes be mapped to the same location in visual representation space, yet similar shapes be mapped to different locations? Without fundamental changes, Chorus, like other single-system models, could not accomplish both mappings in a manner that accounts for recent evidence.


2001 ◽  
Author(s):  
Brett T. Hannigan ◽  
Alastair M. Reed ◽  
Brett A. Bradley

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