A robust digital image watermarking technique using auto encoder based convolutional neural networks

Watermarking plays a very important role in providing authentication, ownership and transmission of secret information. The existing techniques of watermarking in literature are based on either spatial domain or transformation domain. Human brain consists of large number of neurons which are capable of doing paralleling tasking accurately. This resulted in the evaluation of neural network architectures, providing wide range of well connected features representing the input of the network. Convolutional Neural Networks(CNN) which were evolved in early 90s became popular and are being in use in wide range of tasks like classification, detection, recognition, patch matching. In this paper, we propose a digital image watermarking technique using auto-encoder based CNN which is robust to different noises and attacks like salt & pepper, Gaussian and JPEG effect. The proposed method, to the best of our knowledge, is the very first attempt of CNN in the domain of watermarking. We compare the performance of the proposed technique with the existing techniques which are based on spatial domain and transform domain. We show that the proposed method of watermarking using auto encoder based CNN (ACNNWM) gives better or on par results with the existing methods.