Autoencoder-convolutional neural network-based embedding and extraction model for image watermarking

Abstract

Watermarking consists of embedding in, and later extracting from, a digital cover a design called a watermark to prove the image’s copyright/ownership. In watermarking, the use of deep-learning approaches is extremely beneficial due to their strong learning ability with accurate and superior results. By taking advantage of deep-learning, we designed an autoencoder convolutional neural network (CNN)-based watermarking algorithm to maximize the robustness while ensuring the invisibility of the watermark. A two network model, including embedding and extraction, is introduced to comprehensively analyze the performance of the algorithm. The embedding network architecture is composed of convolutional autoencoders. Initially, CNN is considered to obtain the feature maps from the cover and mark images. Subsequently, the feature maps of the mark and cover are concatenated with the help of the concatenation principle. In the extraction model, block-level transposed convolution and the rectified linear unit algorithm is applied on the extracted features of watermarked and cover images to obtain the hidden mark. Extensive experiments demonstrate that the proposed algorithm has high invisibility and good robustness against several attacks at a low cost. Further, our proposed scheme outperforms other state-of-the-art schemes in terms of robustness with good invisibility.