The 11th Iranian and the first International Conference on Machine Vision and Image Processing

Image Watermarking by Q Learning and Matrix Factorization

Mina Alizadeh, Hedieh Sajedi, Bagher Babaali
The 11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020)

Abstract

Today, with the advancement of technology and the widespread use of the internet, watermarking techniques are being developed to protect copyright and data security. The methods proposed for watermarking can be divided into two main categories: spatial domain watermarking, and frequency domain watermarking. Often matrix transformation methods are merged with another method to select the right place to hide. In this paper, a non-blind watermarking id presented. In order to embed watermark Least Significant Bit (LSB) replacement and QR matrix factorization are exploited. Q learning is used to select the appropriate host blocks. The Peak Signal-to-Noise Ratio(PSNR) of the watermarked image and the extracted watermark image is considered as the reward function. The proposed method has been improved over the algorithms mentioned above with no learning methods and achieved a mean PSNR values of 56.61 dB and 55.77 dB for QR matrix factorization and LSB replacemnet embedding method respectively.

Keywords: Watermarking, Q-learning, Steganography, Least Significant Bit Embedding Method, QR Matrix Factorization, Reinforcement Learning



© 2017-2021 ISMVIP All Rights Reserved