Blind color image watermarking incorporating a residual network for watermark denoising and super-resolution reconstruction

Abstract

Watermarking is a technique for hiding secret information in various types of multimedia data to protect intellectual property rights. The integration of deep learning technology with image watermarking is currently reshaping the application and promotion of relevant techniques developed so far. This paper presents a novel type of blind color image watermarking method that embeds a downsized color image into a host color image. Watermarking implementation involves partitioning the host image into non-overlapping blocks of 8 × 8 pixels, performing discrete cosine transform (DCT) for each block of every channel, and then manipulating the magnitudes of three designated DCT coefficients subject to a minimization constraint. The experimental results confirmed that the proposed image watermarking method outperformed six other methods in terms of zero-normalized cross-correlation (ZNCC). Moreover, watermark imperceptibility, as reflected by the measured peak signal-to-noise ratio and mean structural similarity metrics, remained satisfactory. In addition to this new style of color image watermarking, we employed a deep residual network to reduce noise and increase the resolution of the retrieved watermarks. Overall, the residual network achieved a satisfactory ZNCC level (> 0.88) when the watermark images were super-resolved by a factor of sixteen.