Feature-based Image Watermarking Research Articles

A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection.

In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.

A novel robust image watermarking in quaternion wavelet domain based on superpixel segmentation

Feature-based image watermarking is a well-developed area of digital image watermarking. The key advantage of such an approach is the ability to guard against the local desynchronization attacks. However, research in this area usually suffers from the following limitations: (1) The feature points focus too much on the high contrast region and are distributed unevenly. (2) The feature point detector is sensitive to the texture region, and some noise feature points are always detected in the texture region. Based on superpixel image segmentation and quaternion wavelet transform, we propose a digital image watermarking approach which is highly robust against common image processing operations and local geometric transformations. First, the entropy rate superpixel segmentation is used to segment the host image into several homogeneous regions. Then, for each segment region, image feature points are extracted using the SIFER detector, and the affine invariant local regions are constructed adaptively. Finally, the digital watermark is embedded into the local regions by modulating the invariant modulus coefficients. Experimental results are provided to illustrate the efficiency of the proposed image watermarking, especially for noise attacks and local desynchronization attacks.

Desynchronization attacks resilient image watermarking scheme based on global restoration and local embedding

For the existing feature-based image watermarking schemes, it is still a challenge to resist against desynchronization attacks. In this paper, we propose a new feature-based image watermarking scheme for improving the robustness against desynchronization attacks. First, multi-scale Gaussian filtering model is used to extract feature points in original image. The stable and non-overlapped local circular regions centered at feature points are thereafter selected by combining image segmentation and feature points refinement. Finally the watermark is embedded in the Zernike moments of the normalized circular regions. In watermark detection, a suspected image is restored with the geometrical transform matrix which is calculated by feature points matching and RANSAC iteration algorithm. The watermark detection is then conducted in the normalized circular regions after the same feature selection procedure as the embedding one. Preliminary experiments show that the proposed image watermarking can be robust against various attacks including common image processing attacks and desynchronization attacks, even complicated attacks.

Joint robustness and security enhancement for feature-based image watermarking using invariant feature regions

Local image features have been widely applied in feature-based watermarking schemes. The feature invariance is exploited to achieve robustness against attacks, but the leakage of information about hidden watermarks from publicly known locations and sizes of features are often unconsidered in security. This paper, therefore, proposes a novel image watermarking approach, which adopts invariant feature regions to jointly enhance its robustness and security. Initially, circular feature regions are determined by the scale-adapted auto-correlation matrix and the Laplacian-of-Gaussian operation. Leakage of secret information is also controlled carefully during feature detection procedure. An optimal selection process formulated as a multidimensional knapsack problem is then proposed to select robust non-overlapping regions from those circular feature regions to resist various attacks. This process is implemented by a genetic algorithm-based approach, and incorporates randomization to mitigate the security risk. Finally, each selected region is normalized to obtain a geometrically invariant feature region, and embedded with a region-dependent watermark to overcome the weakness of multiple-redundant watermarks. The evaluation results based on the StirMark benchmark present the proposed scheme can tolerate various attacks, including noise-like signal processing and geometric distortions. A security analysis in terms of differential entropy also confirms the security improvement of the proposed method.

Geometric Distortion Insensitive Image Watermarking in Affine Covariant Regions

Feature-based image watermarking schemes, which aim to survive various geometric distortions, have attracted great attention in recent years. Existing schemes have shown robustness against rotation, scaling, and translation, but few are resistant to cropping, nonisotropic scaling, random bending attacks (RBAs), and affine transformations. Seo and Yoo present a geometrically invariant image watermarking based on affine covariant regions (ACRs) that provide a certain degree of robustness. To further enhance the robustness, we propose a new image watermarking scheme on the basis of Seo's work, which is insensitive to geometric distortions as well as common image processing operations. Our scheme is mainly composed of three components: 1) feature selection procedure based on graph theoretical clustering algorithm is applied to obtain a set of stable and nonoverlapped ACRs; 2) for each chosen ACR, local normalization, and orientation alignment are performed to generate a geometrically invariant region, which can obviously improve the robustness of the proposed watermarking scheme; and 3) in order to prevent the degradation in image quality caused by the normalization and inverse normalization, indirect inverse normalization is adopted to achieve a good compromise between the imperceptibility and robustness. Experiments are carried out on an image set of 100 images collected from Internet, and the preliminary results demonstrate that the developed method improves the performance over some representative image watermarking approaches in terms of robustness.