Maximizing Strength of Digital Watermarks Using

Neural Network

Presented by Bin-Cheng Tzeng5/21 2002

Kenneth J.Davis; Kayvan Najarian

International Conference on Neural Networks, 2001. 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 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 parameterci : The coefficient of the original image

mi: The watermark to be added

ci’ : 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 power2.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 100

0 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

Neural Technique for Maximum Watermark

Each image is subdivided into blocks of 64x64 pixels to be treated as a complete image.

4096 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 300-600 iterations or until the mean square error is less than 0.00001

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.