A Watermarking Optimization Method Based on Matrix Decomposition and DWT for Multi-Size Images

Abstract

Image watermarking is a key technology for copyright protection, and how to better balance the invisibility and robustness of algorithms is a challenge. To tackle this challenge, a watermarking optimization method based on matrix decomposition and discrete wavelet transform (DWT) for multi-size images is proposed. The DWT, Hessenberg matrix decomposition (HMD), singular value decomposition (SVD), particle swarm optimization (PSO), Arnold transform and logistic mapping are combined for the first time to achieve an image watermarking optimization algorithm. The multi-level decomposition of DWT is used to be adapted to multi-size host images, the Arnold transform, logistic mapping, HMD and SVD are used to enhance the security and robustness, and the PSO optimized scaling factor to balance invisibility and robustness. The simulation results of the proposed method show that the PSNRs are higher than 44.9 dB without attacks and the NCs are higher than 0.98 under various attacks. Compared with the existing works, the proposed method shows high robustness against various attacks, such as noise, filtering and JPEG compression and in particular, the NC values are at least 0.44% higher than that in noise attacks.

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

“…When discussing the embedding of a watermark in an image, a distinction should be made between embedding a watermark in a static image [ 23 , 34 , 35 , 36 , 37 ] and in a video [ 14 , 15 , 16 , 17 ]. The methods used for static images find their application and development in methods for video purposes.…”

Digital Filter Architecture Based on Modified Winograd Method F(2× 2, 5× 5) and Residue Number System

“…The proposed filter architectures can be applied to digital filters for edge detection [32,33] and smoothing [34], discrete wavelet transform [35], and to implement the convolution operation in the convolutional layer of the convolutional neural network [36].…”

Joint watermarking encryption compression algorithm for securing an e-healthcare application

“…A wavelet transform is a time-frequency domain analysis method that can achieve either time domain or frequency domain localization, and as the characteristics of multi-resolution analysis [10]. Using wavelet transform makes the watermarking more robust and difficult to manipulate [11]. Figure 1 is a schematic diagram of the first-level wavelet decomposition using low-pass and high-pass filters (LPF, HPF).…”