Narrow Results

Image watermarking intends to hide secret data for the purposes of copyright protection, image authentication, data privacy, and broadcast monitoring. The ultimate goal is to achieve highest embedding capacity and lowest image distortion. In this paper, we present an intelligent watermarking scheme which can automatically analyze an image content to extract significant regions of interest (ROIs). A ROI is an area involving crucial information, and will be kept intact. The remaining regions of non-interest (RONIs) are collated for embedding watermarks, and will be ranked according to their entropy fuzzy memberships into a degree of importance. They are embedded by different amounts of bits to achieve optimal watermarking. The watermark is compressed and embedded in the frequency domain of the image. Experimental results show that the proposed technique has accomplished high capacity, high robustness, and high PSNR (peak signal-to-noise ratio) watermarking.

Image watermarking techniques have been widely used for copyright protection, broadcast monitoring, and data authentication. In this paper, we present a novel watermarking scheme which allows automatic selection of multiple regions-of-interest (ROIs) with robustness against geometric distortion. The fidelity of watermarked images is ensured by preserving salient foreground objects. The proposed scheme achieves watermarking robustness by geometric rectification, which is based on matching feature points between the salient foreground objects of a host image and its distorted stego-image. Experimental results show that the proposed technique can successfully obtain high fidelity and high robustness on an image dataset of multiple salient foreground objects.

We present a high-capacity reversible, fragile, and blind watermarking scheme for medical images in this paper. A bottom-up saliency detection algorithm is applied to automatically locate the multiple arbitrarily-shaped regions of interest (ROIs). The iterative square-production algorithm is developed to generate different sizes of squares for shape decomposition on the regions of noninterest (RONIs). This scheme of combining the frequency-domain watermarking and arbitrarily-shaped ROI methods can significantly increase the watermarking capacity, whereas the embedded image fidelity is preserved. Extensive experiments were carried out on the OASIS medical image dataset, which consists of a cross-sectional collection of 416 subjects, aged from 18 to 96 years old. The results show that the proposed scheme outperforms six existing state-of-the-art schemes in terms of watermarking capacity and embedded image fidelity.

In this paper, we present a robust multibit image watermarking scheme to undertake the common image-processing attacks as well as affine distortions. This scheme combines contrast modulation and effective synchronization for large payload and high robustness. We analyze the robustness, payload, and the lower bound of fidelity. Regarding watermark resynchronization under affine distortions, we develop a self-referencing rectification method to detect the distortion parameters for reconstruction by the center of mass in affine covariant regions. The effectiveness and advantages of the proposed scheme are confirmed by experimental results, which show the superior performance as comparing against several state-of-the-art watermarking methods.

In this paper, an algorithm for data embedding in images is proposed. The algorithm is based on using the Discrete Wavelet Transform domain to embed digital data into images. In order to increase the algorithm robustness and ensure the authentication of the data extracted, error detection/correction coding techniques are used to encode the embedded data. The proposed algorithm is blind, i.e., the embedded data are extracted and authenticated without any reference to the original image or data. Experimental results demonstrate the low perceptibility of distortions caused by the data embedding and the high algorithm robustness against several common image-processing operations.

In this paper, we propose an improved autocorrelation function (ACF)-based watermarking that is robust against combined geometric and removal attacks. ACF-based watermarking is known to be effective against geometric attacks. In this watermarking scheme, both the embedded watermark and autocorrelation peaks of the watermark should survive a variety of attacks. However, the autocorrelation peaks are not strong enough. The proposed method improves the robustness of ACF-based watermarking by enhancing the strength of the autocorrelation peaks. Instead of the simple addition of a watermark to an image, the proposed watermark embedder uses the information of an original image more actively during the embedding process, so that the marked image has strong autocorrelation peaks. Experimental results show that the proposed scheme provides better robustness than conventional ACF-based watermarking against a variety of combined geometric-removal attacks.

Watermarking is now considered as an efficient means for assuring copyright protection and data owner identification. Watermark embedding techniques depend on the representation domain of the image (spatial, frequency, and multiresolution). Every domain has its specific advantages and limitations. Moreover, each technique in a chosen domain is found to be robust to specific sets of attack types. So we need to propose more robust domains to defeat these limitations and respect all the watermarking criterions (capacity, invisibility and robustness). In this paper, a new watermarking method is presented using a new domain for the image representation and the watermark embedding: the mathematical Hessenberg transformation. This domain is found to be robust against a wide range of STIRMARK attacks such as JPEG compression, convolution filtering and noise adding. The robustness of the new technique in preserving and extracting the embedded watermark is proved after various attacks types. It is also improved when compared with other methods in use. In addition, the proposed method is blind and the use of the host image is not needed in the watermark detection process.

Nowadays, digital watermarking is employed for authentication and copyright protection. In this paper, a secure image watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD), is proposed. Both LWT and SVD are used as mathematical tools for embedding watermark in the host image. In this work, the watermark is a speech signal which is segmented into shorted portions having the same length. This length is equal to 256 and these different portions constitute the different columns of a speech image. The latter is then embedded into a grayscale or color image (the host image). This procedure is performed in order to insert into an image a confidential data which is in our case a speech signal. But instead of embedding this speech signal directly into the image, we transform it into a matrix and treated it as an image (“a speech image”). Of course, this speech signal transformation permits us to use LWT-2D and SVD to both the host image and the watermark (“a speech image”). The proposed technique is applied to a number of grayscale and color images. The obtained results from peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) computations show the performance of the proposed technique. Experimental evaluation also shows that the proposed scheme is able to withstand a number of attacks such as JPEG compression, mean and median attacks. In our evaluation of the proposed technique, we used another technique of secure image watermarking based on DWT-2D and SVD.

In this paper, we propose how to embed the watermarking to an image under the wavelet transform precisely utilizing an appropriate filter for specifying the embedded position and clustering the variance of multi-resolution representation of an image. This system has a feature of the robustness against alteration attacks of malicious users utilizing the precise detection of embedded watermarks.

A new watermarking approach based on affine Legendre moment invariants (ALMIs) and local characteristic regions (LCRs) which allows watermark detection and extraction under affine transformation attacks is presented in this paper. It is a non-blind watermarking scheme. Original image color image is converted into HSV color space and divided into four parts. LCR is constructed and a set of affine invariants are derived on LCRs based on Legendre moments for each part. These invariants can be used for estimating the affine transform coefficients on the LCRs. ALMIs are used for watermark embedding, detection and extraction as they provide synchronization and invariant feature which is necessary for a robust watermarking scheme. The proposed scheme shows resistance to geometric distortion, cropping, filtering, compression, and additive noise than the existing ALMI based scheme [Alghoniemy, M. and Tewfik, A. H. [2004] "Geometric invariance in image watermarking," IEEE Trans. Image Process13(2), 145–153] and affine geometric moment invariant (AGMI) based scheme [Seo, J. S. and Yoo, C. D. [2006] "Image watermarking based on invariant regions of scale-space representation," IEEE Trans. Signal Process. 54(4), 1537–1549].

Robustness, especially to geometrical distortions is the present problem unresolved in the digital watermarking field. We describe an adaptive watermarking algorithm based on quantization in the DFT domain, which is robust to global geometrical distortions. The watermark was composed of a chaotic sequence, and it was embedded in the magnitudes of the DFT, and the embedded positions were the circle in the centered frequencies. The embedding strength adjusted to the properties of the original image. Watermark extraction was implemented without the original image. Experimental results show that our watermarking scheme is robust to global geometrical attacks, such as scaling, translation and rotation.

Geometric attacks become a serious threat to most existing wavelet-based watermarking by destroying the synchronization between watermark and watermarked images. This paper presents a novel robust watermarking scheme against geometrical distortions. Using invariant centroid (IC) and a feature template in DFT domain, the synchronization can be reconstructed. The double-channel watermarking is embedded into low-frequency band in DWT domain. Experimental results prove that our watermarking scheme has good invisibility and excellent robustness against both normal attacks and geometric attacks; furthermore, this method is blind watermarking.

The fidelity and robustness are two conflicts in digital image watermarking scheme. How to solve their conflicts is an important aspect in digital image watermarking research. Human visual system(HVS) based image watermarking scheme is an appropriate way to solve this problem. However, the way that set several thresholds for classification in the past papers is not accurate enough in simulating human visual system. In this paper, support vector machine is used to simulating human visual system accurately and classify image locations into several types that be determined by human visual system. Besides, the JEPG quantization table is also used to quantize embedding strength for gaining good fidelity in our scheme. The experimental results show that the algorithm this paper proposed has good fidelity and strong robustness.