DESIGN AND PERFORMANCE ANALYSIS OF EMBEDDING A DIGITAL WATERMARK IN A NEURAL NETWORK

Abstract

While neural networks have made considerable progress in the area of digital representation,
training of neural models requires a enormous data and time. It is well known that the use of trained
models as initial weights often leads in less training error than un-pre-trained neural networks. We
propose in this paper a digital watermarking system for neural networks. We formulate a new
challenge: the integration of watermarks into neural networks through mathematical modelling of
discrete cosine transform (DCT) based approach. For discrete wavelet transform (DWT)-based
digital image watermarking algorithms, additional performance enhancements could be obtained by
combining DWT with DCT. Throughout the neural networks, we also describe specifications,
embedded conditions, and attack forms of watermarking (WM). The technique presented here does
not affect the network performance in which a watermark is positioned as the watermark is embedded
while the host network is being trained. Finally, we analyse the performance of designed system with
detailed experiments to demonstrate the potential of neural networks watermarking as the basis for
this research attempt.