Abstract
Much research and development have been made to implement deep neural networks for various purposes with hardware. We implement the deep learning algorithm with a dedicated processor. Watermarking technology for ultra-high resolution digital images and videos needs to be implemented in hardware for real-time or high-speed operation. We propose an optimization methodology to implement a deep learning-based watermarking algorithm in hardware. The proposed optimization methodology includes algorithm and memory optimization. Next, we analyze a fixed-point number system suitable for implementing neural networks as hardware for watermarking. Using these, a hardware structure of a dedicated processor for watermarking based on deep learning technology is proposed and implemented as an application-specific integrated circuit (ASIC).
Highlights
Until recently, watermark embedding for most 2D images has been algorithmically performed, and watermark extracting has been proposed according to the embedding process or by modifying it [1,2,3,4,5,6,7]
The optimizations that we propose include computational optimization for batch normalization and memory optimization using shared memory
The convolution arithmetic consists of multiplication of input feature map (IFM) Ii,j and the weight Wi,j and addition of the multiplication result and the bias B
Summary
Watermark embedding for most 2D images has been algorithmically performed, and watermark extracting has been proposed according to the embedding process or by modifying it [1,2,3,4,5,6,7]. It may not be possible to extract the embedding watermark by the deterministic extracting method To overcome such difficulties, techniques for watermarking using machine learning have been studied [8,9,10,11,12,13,14]. Techniques for watermarking using machine learning have been studied [8,9,10,11,12,13,14] These methods are performed as a network by separating the embedding process and the extracting process
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