Xiang-yang Wang

Robust image watermarking approach using polar harmonic transforms based geometric correction

Geometric distortions that cause displacement between embedding and detection are usually difficult for watermark to survive. It is a challenging work to design a robust image watermarking scheme against geometric distortions. In this paper, we propose a robust image watermarking approach using Polar Harmonic Transforms (PHTs) based geometric correction. The novelty of our approach is that (1) the optimal Nonsubsampled Shearlet Transform (NSST), which can provide nearly optimal approximation for 2D image function, is used to embed digital watermark, and (2) the PHTs are exploited for estimating the geometric distortions parameters in order to permit watermark extraction. Experimental results show that the proposed approach not only provides better imperceptibility and robustness against various attacks (including common image processing operations and geometric distortions), but also yields better watermark detection performance than some state-of-the-art image watermarking schemes.

Quaternion polar complex exponential transform for invariant color image description

Moments and moment invariants have been widely used as a basic feature descriptors in image analysis, pattern recognition, and image retrieval. However, they are mainly used to deal with the binary or gray-scale images, which lose some significant color information. Recently, quaternion techniques were introduced to conventional image moments (including Fourier-Mellin moments, Zernike/Pseudo Zernike moments, and Bessel-Fourier moments, etc.) for describing color images, and some quaternion moment and moment invariants were developed. But, the conventional image moments usually cannot effectively capture the image information, especially the edges. Besides, the kernel computation of them involves computation of a number of factorial terms, which inevitably cause the numerical stability of these moments. Based on effective polar complex exponential transform (PCET) and algebra of quaternions, we introduced the quaternion polar complex exponential transform (QPCET) for describing color images in this paper, which can be seen as the generalization of PCET for gray-level images. It is shown that the QPCETs can be obtained from the PCET of each color channel. We derived and analyzed the rotation, scaling, and translation (RST) invariant property of QPCET. We also discussed the problem of color image retrieval using QPCET. Experimental results are provided to illustrate the efficiency of the proposed color image descriptors.

A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel function

By using PCA and AGMM, probabilistic features are extracted and used for fast image retrieval.By using improved Relief algorithm, all training sample weight values are computed and utilized for feedback.SVM kernel function is optimized dynamically according to the feedback samples weight values. Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback-Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.

Content-based image retrieval using local visual attention feature

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.

Robust copy–move forgery detection using quaternion exponent moments

The detection of forgeries in color images is a very important topic in forensic science. Copy–move (or copy–paste) forgery is the most common form of tampering associated with color images. Conventional copy–move forgeries detection techniques usually suffer from the problems of false positives and susceptibility to many signal processing operations. It is a challenging work to design a robust copy–move forgery detection method. In this paper, we present a novel block-based robust copy–move forgery detection approach using invariant quaternion exponent moments (QEMs). Firstly, original tempered color image is preprocessed with Gaussian low-pass filter, and the filtered color image is divided into overlapping circular blocks. Then, the accurate and robust feature descriptor, QEMs modulus, is extracted from color image block holistically as a vector field. Finally, exact Euclidean locality sensitive hashing is utilized to find rapidly the matching blocks, and the falsely matched block pairs are removed by customizing the random sample consensus with QEMs magnitudes differences. Extensive experimental results show the efficacy of the newly proposed approach in detecting copy–paste forgeries under various challenging conditions, such as noise addition, lossy compression, scaling, and rotation. We obtain the average forgery detection accuracy (F-measure) in excess of 96 and 88% across postprocessing operations, at image level and at pixel level, respectively.

A new keypoint-based copy-move forgery detection for small smooth regions

Copy-move forgery is one of the most common types of image forgeries, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Keypoint based copy-move forgery detection approaches extract image feature points and use local visual features, rather than image blocks, to identify duplicated regions. Keypoint based approaches exhibit remarkable performance with respect to computational cost, memory requirement, and robustness. But unfortunately, they usually do not work well if smooth background areas are used to hide small objects, as image keypoints cannot be extracted effectively from those areas. It is a challenging work to design a keypoint-based method for detecting forgeries involving small smooth regions. In this paper, we propose a new keypoint-based copy-move forgery detection for small smooth regions. Firstly, the original tampered image is segmented into nonoverlapping and irregular superpixels, and the superpixels are classified into smooth, texture and strong texture based on local information entropy. Secondly, the stable image keypoints are extracted from each superpixel, including smooth, texture and strong texture ones, by utilizing the superpixel content based adaptive feature points detector. Thirdly, the local visual features, namely exponent moments magnitudes, are constructed for each image keypoint, and the best bin first and reversed generalized 2 nearest-neighbor algorithm are utilized to find rapidly the matching image keypoints. Finally, the falsely matched image keypoints are removed by customizing the random sample consensus, and the duplicated regions are localized by using zero mean normalized cross-correlation measure. Extensive experimental results show that the newly proposed scheme can achieve much better detection results for copy-move forgery images under various challenging conditions, such as geometric transforms, JPEG compression, and additive white Gaussian noise, compared with the existing state-of-the-art copy-move forgery detection methods.

Local quaternion PHT based robust color image watermarking algorithm

A robust color image feature points detector was proposed by incorporating SIFER detector and color invariance model.The affine invariant local regions were built and selected adaptively according to the local image content variation.A new and robust 2D transform, named quaternion PHT, was introduced to embed digital watermark in the color host image. It is a challenging work to design a robust localized color image watermarking scheme against desynchronization attacks. There are two main drawbacks indwelled in current localized color image watermarking: firstly, the pure gray-based feature points detectors were utilized, in which the important color information is ignored. Secondly, the watermarking algorithms were designed mainly to mark the image luminance component only, in which the significant color channels correlation are neglected. In this paper, we propose a robust color image watermarking algorithm using local quaternion PHT (Polar Harmonic Transform), which is invariant to various noises, local geometric transformations, and color variations. Firstly, the stable color image feature points are extracted by using new color image feature point detector, in which the SIFER (Scale-Invariant Feature detector with Error Resilience) detector and color invariance model are incorporated. Then, the affine invariant local regions are built adaptively according to local image content variation. Finally, the digital watermark is embedded into the local regions by modulating the invariant quaternion PHT modulus coefficients. Experiments are carried out on a color image set collected from Internet, and the extensive experimental works have shown that the proposed color image watermarking is not only invisible and robust against common image processing operations such as median filtering, noise adding, and JPEG compression, but also has conquered those challenging desynchronization attacks.

A novel image watermarking scheme against desynchronization attacks by SVR revision

In digital image watermarking, the watermarks vulnerability to desynchronization attacks has long been a difficult problem. On the basis of support vector regression (SVR) theory and local image characteristics, a novel image watermarking scheme against desynchronization attacks by SVR revision is proposed in this paper. First, some pixels are randomly selected and the sum and variance of their neighboring pixels are calculated; second, the sum and variance are regarded as the training features and the pixel values as the training objective; third, the appropriate kernel function is chosen and trained, a SVR training model will be obtained. Finally, the sum and variance of all pixels neighboring pixels are selected as input vectors, the actual output can be obtained by using the well-trained SVR, and the digital watermark can be recovered by judging the output vector. Experimental results show that the proposed scheme is invisible and robust against common signals processing such as median filtering, sharpening, noise adding, and JPEG compression, etc., and robust against desynchronization attacks such as rotation, translation, scaling, row or column removal, shearing, local random bend, etc.

Invariant color image watermarking approach using quaternion radial harmonic Fourier moments

Moments and moment invariants have become a powerful tool in gray image watermarking. More recently, a few moment-based approaches were developed to embed watermark into color host image by marking the luminance component or three color channels, and they always cannot obtain better imperceptibility and robustness because of ignoring the correlation between different color channels. Quaternion is a generalization of the complex numbers, and can treat a color image as a vector field without losing color information. In this paper, based on algebra of quaternions and radial harmonic Fourier moments (RHFMs), we introduced quaternion radial harmonic Fourier moments (QRHFMs) for color images, which can be seen as the generalization of RHFMs for gray-level images. We analyzed and discussed the geometric invariant property of QRHFMs, and proposed a geometric invariant color image watermarking scheme using QRHFMs. Experimental results show that the proposed watermarking scheme not only provides better imperceptibility and robustness against various attacks (including common image processing operations and geometric distortions), but also yields better watermark detection performance than some state-of-the-art image watermarking schemes.

Image retrieval based on exponent moments descriptor and localized angular phase histogram

Multiple feature extraction and combination is one of the most important issues in the content-based image retrieval (CBIR). In this paper, we propose a new content-based image retrieval method based on an efficient combination of shape and texture features. As its shape features, exponent moments descriptor (EMD), which has many desirable properties such as expression efficiency, robustness to noise, geometric invariance, fast computation etc., is adopted in RGB color space. As its texture features, localized angular phase histogram (LAPH) of the intensity component, which is robust to illumination, scaling, and image blurring, is used in hue saturation intensity (HSI) color space. The combination of above shape and texture information provides a robust feature set for color image retrieval. Experimental results on well known databases show significant improvements in retrieval rates using the proposed method compared with some current state-of-the-art approaches.