Image Watermarking by Q Learning and Matrix Factorization
Paper ID : 1137-MVIP2020
Mina Alizadeh *, Hedieh Sajedi, Bagher Babaali
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 study, a new watermarking method using the Q learning algorithm is presented. The proposed model using the Q learning algorithm to select the best location in an image for hiding a secret image. The Peak Signal-to-Noise Ratio(PSNR) of the watermarked image and the extracted watermark image is considered as the reward function. The embedding method is performed by using the Least Significant Bit (LSB) algorithms and QR matrix factorization. The proposed method has been improved over the algorithms mentioned above with no learning methods.
Watermarking, Q-learning, Steganography, Least Significant Bit embedding method, QR matrix factor- ization, Reinforcement learning
Status : Paper Accepted (Oral Presentation)