For example, the 'blindness' property that helps determine the requirements of other inputs (different from the input image and watermark), which are not considered in the model.
Therefore, analyzing various security issues (e.g., vector quantization attacks [16] arising from an input image independent watermark generation), and abstraction of new schemes (which are not spread-spectrum communication based) may require a further development of that model.
Otherwise, D returns ĩ, w ~, which indicates that the input image is watermarked.
The schemes proposed in [5, 6] assume that the kth client produces as input to the watermarking protocol a random string of L bits denoted as b k, representing his/her secret.
Input: An original image, watermark.
For an image application, the generation function, G, can take image data, i, and message, m and/or other image data, j as input, and outputs a watermark, w.
Check 'Include watermark' in the 'Watermark' section.
It is worth noting here that we consider the original (unwatermarked) version of an image as the input image for the watermarking functions.
In other words, by determining the required (watermarking) inputs, outputs, and properties for different objectives, this model helps characterize a watermarking scheme.
The challenge here is to consider a 'complete' set of watermarking inputs, outputs, and component functions in general from their specific information domains and function families.
