Image Watermarking with Region of Interest Determination Using Deep Neural Networks

Watermarking is a popular technique used in various applications, such as copyright protection of digital media, including audio, video, and image files. Proper watermarking should satisfy multiple criteria, such as robustness and transparency. While a successful watermarking needs to meet these criteria, there is a tradeoff between the two opposing criteria of robustness and transparency. This paper proposes a method for determining the appropriate locations for embedding watermarks with high strength factors. For this purpose, a deep neural network, known as Mask R-CNN, is used, which is pre-trained on the COCO dataset. This neural network finds a good strength factor for those sub-blocks of the host image selected for embedding. The proposed technique can be used in conjunction with most DWT and DCT based semi-blind watermarking approaches. Experiments show that the proposed method is robust against different attacks and demonstrates good transparency.