This paper proposes a novel screen-shooting resilient watermarking scheme, which means that if the watermarked image is displayed on the screen and the screen information is captured by the camera, we can still extract the watermark message from the captured photo. To realize such demands, we analyzed the special distortions caused by the screen-shooting process, including lens distortion, light source distortion, and moire distortion. To resist the geometric deformation caused by lens distortion, we proposed an intensity-based scale-invariant feature transform (I-SIFT) algorithm which can accurately locate the embedding regions. As for the loss of image details caused by light source distortion and moire distortion, we put forward a small-size template algorithm to repeatedly embed the watermark into different regions, so that at least one complete information region can survive from distortions. At the extraction side, we designed a cross-validation-based extraction algorithm to cope with repeated embedding. The validity and correctness of the extraction method are verified by hypothesis testing. Furthermore, to boost the extraction speed, we proposed a SIFT feature editing algorithm to enhance the intensity of the keypoints, based on which, the extraction accuracy and extraction speed can be greatly improved. The experimental results show that the proposed watermarking scheme achieves high robustness for screen-shooting process. Compared with the previous schemes, our algorithm provides significant improvement in robustness for screen-shooting process and extraction efficiency.

Screen-Shooting Resilient Watermarking

In this paper, we present a new joint watermarking-compression scheme the originality of which stands in the combination of the lossless compression standard JPEG-LS with the bit substitution watermarking modulation. This scheme allows the access to watermarking-based security services without decompressing the image. It becomes possible to trace images or to verify their authenticity directly from their compressed bitstream. Performance of our scheme, expressed in terms of embedding capacity and distortion, are evaluated on ultrasound images. They show that the watermarked images do not perceptually differ from their original counterparts while offering a capacity large enough to support various security services.

Joint watermarking and lossless JPEG-LS compression for medical image security

Imperceptible reversible watermarking of radiographic images based on quantum noise masking.

This paper proposes a novel robust image watermarking scheme in Discrete Cosine Transform (DCT) domain for active forensics. We investigate the relation between the positions and the modification magnitudes of DCT coefficients and directions of texture blocks. By exploring such relation, a direction-coefficient mapping is designed. First, the texture direction of each image block is estimated by Gabor filter. And then, according to the direction-coefficient mapping, one watermark bit is embedded into each block along its texture direction. Compared with existing schemes, the proposed method utilizes the direction features of texture blocks better . Therefore, the improvements in watermarked image quality and the robustness of watermark signal against image processing attacks are both achieved.

A robust image watermarking scheme in DCT domain based on adaptive texture direction quantization

A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize.

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Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

This paper propose a novel adaptive multi-bit digital image watermarking algorithm based on discrete cosine transform and human visual system. This algorithm has several characteristics: first, embed 2 bits water-marking information into one embedding point is realized. Second, HVS property is used to embed watermark adaptively in the texture direction of visual insensitive area. Experimental results show that the proposed watermarking scheme is imperceptible and robust against various signal processing such as cropping, JPEG compression, filtering, noise adding and image enhancement, etc.

A Novel Multi-bit Watermarking Algorithm Based on HVS

Feature matching is a critical and challenging process in feature-based image registration. In this paper, a robust feature point matching method, combined Scale-Invariant Feature Transform (SIFT) and Support Vector Machine (SVM), is proposed to match features for registering dynamic aerial images. In this method, every feature point is described by 128 dimensional SIFT descriptor as a training vector. Then feature matching model is built by SVM. Using this model, feature points are classified into two categories, one is matched feature set and the other is unmatched feature set. Three pairs of infrared (IR) and ultraviolet (UV) aerial images are utilized to evaluate the performance. The matching results have confirmed that the proposed method can match the feature points exactly even with a lot of outliers.

A Robust Feature Point Matching Method for Dynamic Aerial Image Registration

Edge detection is a crucial method for the location and quantity estimation of oil slick when oil spills on the sea In this paper, we present a robust active contour edge detection algorithm for oil spill remote sensing images In the proposed algorithm, we define a local Gaussian data fitting energy term with spatially varying means and variances, and this data fitting energy term is introduced into a global minimization active contour (GMAC) framework The energy function minimization is achieved fast by a dual formulation of the weighted total variation norm The proposed algorithm avoids the existence of local minima, does not require the definition of initial contour, and is robust to weak boundaries, high noise and severe intensity inhomogeneity exiting in oil slick remote sensing images Furthermore, the edge detection of oil slick and the correction of intensity inhomogeneity are simultaneously achieved via the proposed algorithm The experiment results have shown that a superior performance of proposed algorithm over state-of-the-art edge detection algorithms In addition, the proposed algorithm can also deal with the special images with the object and background of the same intensity means but different variances

A Robust Active Contour Edge Detection Algorithm Based on Local Gaussian Statistical Model for Oil Slick Remote Sensing Image

An illumination and affine invariant descriptor is proposed for registering aerial images with large illumination changes and affine transformation, low overlapping areas, monotonous backgrounds or similar features. Firstly, triangle region is detected by K-nearest neighbors (K-NN) graph of initial matched result by Scale-Invariant Feature Transform (SIFT). In order to improve the accuracy, region growth is applied to boost small and slender triangles. Then illumination and affine invariant descriptor is defined to describe triangle regions and measure their similarity. The descriptor named as IIMSA is the combination of MultiScale Autoconvolution (MSA) and multiscale retinex (MSR). The performance of the descriptor is evaluated with optical aerial images and the experimental results demonstrate that the proposed descriptor IIMSA is more distinctive than MSA and SIFT.

An illumination and affine invariant descriptor for aerial image registration

Active contour model is a typical and effective closed edge detection algorithm, which has been widely applied in remote sensing image processing. Since the variety of the image data source, the complexity of the application background and the limitations of edge detection, the robustness and universality of active contour model are greatly reduced in the practical application of edge extraction. This study presented a fast edge detection approach based on global optimization convex model and Split Bregman algorithm. Firstly, the proposed approach defined a generalized convex function variational model which incorporated the RSF model’s principle and Chan’s global optimization idea and could get the global optimal solution. Secondly, a fast numerical minimization scheme based on split Bregman iterative algorithm is employed for overcoming drawbacks of noise and others. Finally, the curve evolves to the target boundaries quickly and accurately. The approach was applied in real special sea ice SAR images and synthetic images with noise, fuzzy boundaries and intensity inhomogeneity, and the experiment results showed that the proposed approach had a better performance than the edge detection methods based on the GMAC model and RSF model. The validity and robustness of the proposed approach were also verified.

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Zhaoxia Liu

Papers

A Novel Multi-bit Watermarking Algorithm Based on HVS

A Robust Feature Point Matching Method for Dynamic Aerial Image Registration

A Robust Active Contour Edge Detection Algorithm Based on Local Gaussian Statistical Model for Oil Slick Remote Sensing Image

An illumination and affine invariant descriptor for aerial image registration

Fast edge detection approach based on global optimization convex model and split Bregman algorithm