Maximizing Strength of Digital Watermarks Using Neural Network Presented by Bin-Cheng Tzeng 5/ Kenneth J.Davis; Kayvan Najarian International Conference on Neural Networks, Proceedings.

Outlines Introduction A Watermarking Technique in the DWT Domain Neural Technique for Maximum Watermark Conclusions

Introduction For watermarking to be successful 1.Unobtrusive 2.robust In other words, one would like to insert the watermark with maximum strength before it becomes visible to the human visual system(HVS)

Introduction(Cont.) The way the strength of the added watermark is chosen is of highest importance. This paper attempts to define a neural network based algorithm to automatically control and select the watermarking parameters to create maximum-strength watermarks.

A Watermarking Technique in the DWT Domain The paper use a wavelet-based scheme for digital watermarking. (reference “ A New Wavelet-Based Scheme for Watermarking Images ” ) The technique was tested by cropping, JPEG compression, Gaussian noise, halfsizing, and median filtering.

A Watermarking Technique in the DWT Domain

A threshold was used to determine the significant coefficients. The watermark is added to the significant coefficients of all the bands other than the low pass subband.

A Watermarking Technique in the DWT Domain  : The scaling parameter c i : The coefficient of the original image m i : The watermark to be added c i ’ : the watermarked coefficient

Neural Technique for Maximum Watermark To achieve maximal watermarking while remaining invisible to the human eye. 1.Generating a watermarked image using a given power 2.allowing one or more persons to judge the image,repeat while increasing the power until the humans deem the watermark visible

Neural Technique for Maximum Watermark Replacing the humans in the process with a neural network allowing the process to be automated. To train the neural network, a database of original and watermarked images whose qualities are judged by several human subjects is being created.

Neural Technique for Maximum Watermark When judging the images, a score is given between 0 and means no perceivable difference between the original image and watermarked image and 100 means the watermark has highly distorted the image.

Neural Technique for Maximum Watermark Feed forward back-propagation network Being able to properly approximate non-linear functions and if properly trained will perform reasonably well when presented with inputs it has not seen before HVS is non-linear To be useful.

Neural Technique for Maximum Watermark

Each image is subdivided into blocks of 64x64 pixels to be treated as a complete image inputs and 1 final input (  ) The hidden layer with 256 or 512 neurons

Neural Technique for Maximum Watermark The network is trained using the scaled conjugate gradient algorithm(SCG) Trained for iterations or until the mean square error is less than

Comparison of Neural Network and Human watermark visibility scores

Conclusions The watermark is added to both low and high scales of DWT. To aid in maximizing the watermark a neural network that mimics the HVS was proposed. When properly trained, the neural network can allow it to be used in place of several human reviewers.