Maximizing Strength of Digital Watermarks Using Neural Network

1. Maximizing Strength of Digital Watermarks Using Neural Network Kenneth J.Davis; Kayvan Najarian International Conference on Neural Networks, 2001. Proceedings. Presented by Bin-Cheng Tzeng 5/21 2002

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

3. Introduction • For watermarking to be successful1.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)

4. 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.

5. 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.

6. A Watermarking Technique in the DWT Domain

7. 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.

8. A Watermarking Technique in the DWT Domain  : The scaling parameter ci : The coefficient of the original image mi: The watermark to be added ci’ : the watermarked coefficient

9. 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

10. 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.

11. 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.

12. 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.

13. Neural Technique for Maximum Watermark

14. 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

15. 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

16. Comparison of Neural Network and Human watermark visibility scores

17. 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.