Detecting Digital Watermarking Image Attacks Using a Convolution Neural Network Approach

Abstract

In scientific research, one of the most significant problems of recent years has been and continues to be the protection of digital material. The advancement of Internet technology has allowed for the illicit duplication, authentication, and distribution of digital material by unauthorized individuals. For this reason, a variety of watermarking systems have been investigated for a variety of purposes, including broadcast monitoring, intellectual property protection, content authentication, and copy control. There are various types of the watermarking image attacks that impact the quality of the images; therefore, it is critical to ensure that watermarked digital images can withstand these kinds of attacks. Hence, novelty of the proposed research is to develop approaches to detect these attacks which becomes very important to guarantee a sufficient quality of watermarking images. In this paper, a deep learning method based on a convolution neural network (CNN) algorithm was proposed to detect various types of watermarking attacks, namely, median filter, Gaussian filter, salt-and-pepper, average filter, motion blur, and no attack, to improve the watermarking quality. Evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the normalization correlation (NC) were employed to examine the invisibility and robustness of the watermarking images. The empirical results of the CNN model show good performance for detecting watermarking attacks with different sizes (256, 128, and 64). The accuracy percentage of the testing process was 98%. A highly efficient CNN approach was developed. Very high performance of NC was found in the detection of the salt-and-pepper attack (99.02%, 99.97%, and 99.49% with respect to watermarking image sizes of 256 × 256, 128 × 128, and 64 × 64, respectively). The study concludes that the CNN model is able to detect watermarking attacks successfully.