Maximizing Strength of Digital Watermarks Using Neural Network Presented by Bin-Cheng Tzeng 5/21...
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Transcript of Maximizing Strength of Digital Watermarks Using Neural Network Presented by Bin-Cheng Tzeng 5/21...
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.