Abstract—Digital watermarking is one of the most widely used techniques for the protection of ownership rights of digital audio, images, and videos. One of the desirable properties of a digital watermarking scheme is its robustness against attacks aiming at removing or destroying the watermark from the host data. Different from the common watermarking techniques based on the spatial domain or transform domain, in this paper, a novel scheme of digital image blind watermarking based on the combination of the discrete wavelet transform (DWT) and the convolutional neural network (CNN) is proposed. Firstly, the host images are decomposed by the DWT with 4 levels and, then, the low frequency sub-bands of the first level and the high frequency sub-bands of the fourth level are used as the input data and the output target data to train the CNN model for embedding and extracting the watermark. Experimental results show that the proposed scheme has superior performance against common attacks of JPEG compression, mean and median filtering, salt and pepper noise, Gaussian noise, speckle noise, brightness modification, scaling, cropping, rotation, and shearing operations. Index Terms—Robust image watermarking, discrete wavelet transform, convolutional neural network, copyright protection. I. INTRODUCTION The explosive growth of the Internet and the social networks has provided the increasing convenience for the transmission and sharing digital multimedia applications such as audio, images and videos. With the development of the advanced multimedia signal processing technologies, digital multimedia can be easily and simply acquired, copied and tampered. Thus, the issues related to multimedia information protection, copyright and content authentication have been of significant concerns [1]. With digital image data, there are extensive studies on how to prevent unauthorized users from illegally copying, and distributing, modifying the digital images [1], [2]. The digital watermarking techniques which embed hidden information (known as a watermark) to a host media to detect and trace copyright violations have attracted considerable interest from Manuscript received September 3, 2019; revised June 21, 2020. Nguyen Chi Sy and Ha Hoang Kha are with the Faculty of Electrical & Electronics Engineering, Ho Chi Minh City University of Technology, VNU-HCM, Vietnam (e-mail: chisy.nguyen@gmail.com, hhkha@hcmut.edu.vn). Nguyen Minh Hoang is with the Faculty of Management Information Systems, Banking University of HCM City, Vietnam (e-mail: nmhoang@gmail.com). academia and industry [3]. Digital image watermarking can be found in various practical applications of copyright protection, image authentication, medical applications, tamper detection, digital fingerprinting [4]. The most important and desirable properties in applications of watermarking for protecting the owners’ copyright are invisibility and robustness. Invisibility measures the changes in the quality of host images before and after watermarking. Robustness measures the ability that the embedded watermarks cannot be destroyed and removed by the signal processing operations. In general, there is a trade-off between invisibility and robustness [5]. Based on the specific applications, a watermarking technique can be appropriately chosen to obtain the desired properties. Based on the domain in which the watermark is embedded, digital image watermarking techniques can typically be divided into different categories such as the spatial domain [6], [7], transform domain [8]-[10] or hybrid ones [11]. In spatial domain watermarking schemes, the watermarks are inserted in the host images in the spatial domain by modifying the gray level values of chosen pixels in images [7]. Although the spatial domain watermarking is simple to implement, it can be sensitive to common attacks such as JPEG compression, low-pass filtering, and the watermarks can be easily de-attached by using inverse operations [6], [12], [13]. Therefore, spatial domain watermarking techniques are not commonly used in many practical applications. Alternatively, to improve the robustness and imperceptibility, watermarking can be carried out in transform domains such as the fast Fourier transform (FFT) [14], discrete cosine transform (DCT) [15], discrete wavelet transform (DWT) [13], [16], DWT-DCT [17], [18]. The transform domain watermarking can offer better robustness against common attacks since watermark coefficients are spread over the host image. More recently, to further enhance the imperceptibility of watermarked images and robustness of watermarks, artificial intelligence (AI) based methods in digital image watermarking have attracted great interests, see, for examples [19]-[24] and references therein. In [19], the authors introduced the blind watermarking scheme exploiting the back-propagation (BP) neural network (NN) in the DWT domain. The authors demonstrated that their algorithm offers imperceptibility and robustness to common attacks such as salt and pepper noise, median filtering, rotation, cropping and JPEG compression. Similarly, the authors in [20] studied on a blind watermarking algorithm An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain Nguyen Chi Sy, Ha Hoang Kha, and Nguyen Minh Hoang International Journal of Machine Learning and Computing, Vol. 10, No. 5, September 2020 675 doi: 10.18178/ijmlc.2020.10.5.990