A comprehensive survey on robust image watermarking

The screen-cam process, which is taking pictures of the content displayed on a screen with mobile phones or cameras, is one of the main ways that image information is leaked. However, traditional image watermarking methods are not resilient to screen-cam processes with severe distortion. In this paper, a screen-cam robust watermarking scheme with a feature-based synchronization method is proposed. First, the distortions caused by the screen-cam process are investigated. These distortions can be summarized into the five categories of linear distortion, gamma tweaking, geometric distortion, noise attack, and low-pass filtering attack. Then, a local square feature region (LSFR) construction method based on a Gaussian function, modified Harris–Laplace detector, and speeded-up robust feature (SURF) orientation descriptor is developed for watermark synchronization. Next, the message is repeatedly embedded in each selected LSFR by an improved embedding algorithm, which employs a non-rotating embedding method and a preprocessing method, to modulate the discrete Fourier transform (DFT) coefficients. In the process of watermark detection, we fully utilize the captured information and extract the message based on a local statistical feature. Finally, the experimental results are presented to illustrate the effectiveness of the method against common attacks and screen-cam attacks. Compared to the previous schemes, our scheme has not only good robustness against screen-cam attack, but is also effective against screen-cam with additional common desynchronization attacks.

Screen-Cam Robust Image Watermarking with Feature-Based Synchronization

With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.

/pdf/robust-zero-watermarking-algorithm-for-medical-images-using-3fstb07h.pdf

Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition

A novel block‐level perceptual image watermarking framework is proposed in this study, including tri‐directional correlation and a block‐level just noticeable difference (JND) model. Specifically, the difference in the discrete cosine transform (DCT) coefficients of two blocks is calculated based on three directions in the neighborhood, called the tri‐directional correlation (TriDC). Additionally, the representative alternating current (AC) coefficients along horizontal, vertical, and diagonal directions, which can describe structural patterns, are projected and merged for TriDC differences. Then, the difference of the DCT coefficient is modulated to a predefined zone depending on the JND‐based offset. Finally, the extent of the watermarked AC coefficients is determined with perceptual JND adjustment. The experimental results demonstrate that the proposed scheme can protect most common image processing attacks; and has better robustness compared with recent zone modulation watermarking schemes and traditional watermarking methods.

JND-aware robust image watermarking with tri-directional inter-block correlation

Neural style transfer for image steganography and destylization with supervised image to image translation

In the robust image watermarking framework, watermarks are usually embedded in the direct current (DC) coefficients in discrete cosine transform (DCT) domain, since the DC coefficients have a larger perceptual capacity than any alternating current (AC) coefficients. However, DC coefficients are also excluded from watermark embedding with the consideration of avoiding block artifacts in watermarked images. Studies on human vision suggest that perceptual characteristics can achieve better image fidelity. With this perspective, we propose a novel spatial–perceptual embedding for a color image watermarking algorithm that includes the robust just-noticeable difference (JND) guidance. The logarithmic transform function is used for quantization embedding. Meanwhile, an adaptive quantization step is modeled by incorporating the partial AC coefficients. The novelty and effectiveness of the proposed framework are supported by JND perceptual guidance for spatial pixels. Experiments validate that the proposed watermarking algorithm produces a significantly better performance.

https://www.mdpi.com/2227-7390/8/9/1506/pdf

Spatial-Perceptual Embedding with Robust Just Noticeable Difference Model for Color Image Watermarking

Improving the perceptual quality of images is challenging for the robust watermarking method. To address this problem, most existing algorithms proposed to guide the watermark embedding based on just noticeable difference (JND) model, which mainly consists of corresponding contrast masking factor. However, existing metrics are mostly based on the intra-block texture energy, which cannot exactly describe the texture information and meet the needs of watermarking robustness. In this paper, we present a new texture metric, which incorporates the visual saliency based inter-block texture regularity with the robust discrete cosine transform (DCT) coefficients. The JND model is constructed as a modulation factor by using the proposed robust texture metric, which embeds the intra-block energy and inter-block regularity measurement to capture local and global features. Based on the proposed model, we introduce an adaptive quantization step for the watermarking framework. Both quantitative and qualitative results show that our scheme achieves better performance in comparison with several state-of-the-art schemes. 

Yunming Zhang

Papers

A comprehensive survey on robust image watermarking

JND-aware robust image watermarking with tri-directional inter-block correlation

Spatial-Perceptual Embedding with Robust Just Noticeable Difference Model for Color Image Watermarking

Towards perceptual image watermarking with robust texture measurement