Image watermarking is an important tool for copyright management, and various watermarking methods have been proposed. Tensor decomposition, which has attracted much attention in image processing, has been applied as a watermarking method, however, no method has been proposed for color cover images with grayscale watermarks. Therefore, we propose a color image watermarking method that involves using a grayscale watermark. We evaluated the attack resistance of the proposed method through experiments.

Perceptual hashing, which generates a message digest showing how humans perceive similarity in images, is suitable for ensuring the equivalence between a modified/edited image and the original. Conventional perceptual hashing is mainly utilized for similarity-based image retrieval and is not appropriate for image identification, which is required in security systems such as digital rights management. We previously developed a construction method for perceptual hashing in security systems that utilizes a convolutional neural network (CNN). In practical applications, multiple different images are published in various media forms (e.g., articles or books), so generating an identical message digest for each of these images simultaneously makes content management easier. Therefore, in this work we extend our earlier CNN-based perceptual hashing scheme so that it can generate an identical message digest for images in a group. This approach reduces the computational cost for fine-tuning CNN compared to generating a perceptual hash for each image in a group individually.

Although non-local image denoising has attracted much research effort due to its superior performance, little attention has focused on its color extension. Most existing non-local color image denoising methods process the color channels of an input image separately. However, in order to improve the performance of color image denoising, all color channels should be processed jointly for fully utilizing the interchannel dependency. This paper proposes a new non-local and inter-channel dependency aware prior, named weighted tensor nuclear norm (WTNN), and it is defined on a 3rd-order tensor from a patch cluster of an input image. We also present an effective algorithm for color image denoising using the WTNN. Experimental results clearly show that the proposed algorithm outperforms a state-of-the-art color image denoising method, known as CBM3D.