II. Related Works
2.1 Visible watermarking
Visible watermarking techniques are used to protect copyright of digital multimedia (audio, image or video) that have to be delivered for certain purpose, such as digital multimedia used in exhibition, digital library, advertisement or distant learning web, while illegal duplicate is forbidden. From the literature survey, the visible watermarking has captured greater attention than the invisible one [9] since there are not only different visible watermarking approaches either in spatial or transform domain but also various visible watermark removal schemes. We will briefly address different visible watermark techniques here and the removal schemes will be further discussed in Section IV.
Braudaway et al. [4] proposed one of the early approaches for visible watermarking by formulating the nonlinear equation to accomplish the luminance alteration in spatial domain.
In this scheme, dimensions of watermark image are equal to those of the host image. There is a one-to-one correspondence between pixel locations in the watermark image and those in the host image. According to their brightness, pixels in the watermark image can be divided into transparent and nontransparent ones. The brightness of each pixel in the host image in proportion to the nontransparent regions of the watermark will be increased or reduced to a perceptually equal amount by using nonlinear equation while the brightness of each pixel in proportion to the transparent regions of the watermark will remain the same after watermark embedding. They formulate the nonlinear equation by using an approximately color space, such as the CIE 1976 (L*u*v*) space and the CIE (L*a*b*) space and various parameters of the nonlinear equation are applied to make the watermark difficult to remove.
Meng and Chang [10] applied the stochastic approximation for Braudaway's method in
the discrete cosine transform (DCT) domain by adding visible watermarks in video sequences.
Mohanty et al. [11] proposed a watermarking technique called dual watermarking by combining of a visible watermark and an invisible watermark in the spatial domain. The visible watermark adopted to establish the owner’s right to the image and invisible watermark to check the intentional and unintentional tampering of the image. Chen [12] has proposed a visible watermarking mechanism to embed a gray level watermark into the host image based on a statistic approach. First, the host image is divided into equal blocks and the standard deviation in each block is calculated. The standard deviation value will determine the amount of gray value of the pixel in the watermark to be embedded into the corresponding host image.
Kankanhalli et al. [13] proposed a visible watermarking algorithm in the discrete cosine transform (DCT) domain. First, the host image and the watermark image are divided into 8x8 blocks. Then, they classify each block into one of 8 classes depending on the sensitivity of the block to distortion and adopted the effect of luminance to make a final correction to the block scaling factors. The strength of the watermark is added in varying proportions depending on the class to which the image block belongs. Kankanhalli et al. [14] proposed a modification to their above watermark insertion technique to make the watermark more robust.
Hu and Kwong [15-16] implemented an adaptive visible watermarking in the wavelet domain by using the truncated Gaussian function to approximate the effect of luminance masking for the image fusion. Based on image features, they first classify the host and watermark image pixels into different perceptual classes. Then, they use the classification information to guide pixel-wise watermark embedding. In high-pass subbands, they focus on image features, while in the low-pass subband, they use truncated Gaussian function to approximate the effect of luminance masking. Yong et al. [17] also proposed a translucent digital watermark in the DWT domain and use error-correct code to improve the ability to anti-attack.
Each of above schemes wasn’t devoted to better feature-based classification and the use of sophisticated visual masking models, so Huang and Tang [9] presented a contrast sensitive visible watermarking scheme with the assistance of HVS. They first compute the CSF mask of the discrete wavelet transform domain. Secondary, they use square function to determine the mask weights for each subband. Third, they adjust the scaling and embedding factors based on the block classification with the texture sensitivity of the HVS. However, their scheme doesn’t consider the following issues:
1. The basis function of the wavelet transform plays an important role during the application of CSF for the HVS in the wavelet transform domain.
2. The embedding factors emphasize more weights in the low frequency domain instead of the medium-to-high frequency domain.
3. The interrelationship of block classification and the characteristics of the embedding location.
For issues one, the direct application of CSF for the HVS in the wavelet transform domain needs to be further studied [18, 19, 20] while the basis function of the wavelet transform is a critical factor to affect the visibility of the noise in the DWT domain. For issue two, the watermark embedding in the low frequency components results high degradation of the image fidelity. In addition, the high frequency components of the watermarked image easily suffer common image signal processing attacks with low robustness. For issue 3, the plane, edge and texture block classification in [9] is a genuine approach should the local and global characteristics of wavelet coefficients be further considered.