Common Image Processing Operations Research Articles

Robust reversible watermarking of JPEG images

Robust reversible watermarking (RRW) is a potential technology for copyright protection and integrity authentication due to its ability in both robustness and reversibility. Currently, there is no detailed report on RRW of JPEG images. In this paper, we propose an RRW algorithm of JPEG images by selecting quantized discrete cosine transform coefficients to construct robust features for watermarking. In the proposed algorithm, a watermark sequence can be embedded into a JPEG image by shifting the histogram of its constructed robust features. For less embedding distortion, smaller file size, and higher structural similarity, an evaluation method has been designed to select those appropriate frequency bands for watermarking. On the receiver side, the watermark can be accurately extracted by reconstructing the robust features, and the original JPEG image can be restored losslessly by performing the inverse operation of the histogram shifting. Experimental results show that the watermark is robust to common image processing operations (e.g., JPEG recompression, JPEG2000 compression, WebP compression, and additive white Gaussian noise), and can effectively resist those attacks from the lossy channels in real life, such as the most popular apps Instagram and WeChat.

The incremental online k-means clustering algorithm and its application to color quantization

Color quantization is a common image processing operation with various applications in computer graphics, image processing, and computer vision. Color quantization is essentially a large-scale combinatorial optimization problem. Many clustering algorithms, both of hierarchical and partitional types, have been applied to this problem since the 1980s. In general, hierarchical color quantization algorithms are faster, whereas partitional ones produce better results provided that they are initialized properly. In this paper, we propose a novel partitional color quantization algorithm based on a binary splitting formulation of MacQueen’s online k-means algorithm. Unlike MacQueen’s original algorithm, the proposed algorithm is both deterministic and free of initialization. Experiments on a diverse set of public test images demonstrate that the proposed algorithm is significantly faster than two popular batch k-means algorithms while yielding nearly identical results. In other words, unlike previously proposed k-means variants, our algorithm addresses both the initialization and acceleration issues of k-means without sacrificing the simplicity of the algorithm. The presented algorithm may be of independent interest as a general-purpose clustering algorithm.

Restorable Image Operators with Quasi-Invertible Networks

Image operators have been extensively applied to create visually attractive photos for users to share processed images on social media. However, most image operators often smooth out details or generate textures after the processing, which removes the original content and raises challenges for restoring the original image. To resolve this issue, we propose a quasi-invertible model that learns common image processing operators in a restorable fashion: the learned image operators can generate visually pleasing results with the original content embedded. Our model is trained on input-output pairs that represent an image processing operator's behavior and uses a network that consists of an invertible branch and a non-invertible branch to increase our model's approximation capability. We evaluate the proposed model on ten image operators, including detail enhancement, abstraction, blur, photographic style, and non-photorealistic style. Extensive experiments show that our approach outperforms relevant baselines in the restoration quality, and the learned restorable operator is fast in inference and robust to compression. Furthermore, we demonstrate that the invertible operator can be easily applied to practical applications such as restorable human face retouching and highlight preserved exposure adjustment.

Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry

As an efficient watermark attack method, geometric distortions destroy the synchronization between watermark encoder and decoder. And the local geometric distortion is a famous challenge in the watermark field. Although a lot of geometric distortions resilient watermarking schemes have been proposed, few of them perform well against local geometric distortion like random bending attack (RBA). To address this problem, this paper proposes a novel watermark synchronization process and the corresponding watermarking scheme. In our scheme, the watermark bits are represented by random patterns. The message is encoded to get a watermark unit, and the watermark unit is flipped to generate a symmetrical watermark. Then the symmetrical watermark is embedded into the spatial domain of the host image in an additive way. In watermark extraction, we first get the theoretically mean-square error minimized estimation of the watermark. Then the auto-convolution function is applied to this estimation to detect the symmetry and get a watermark units map. According to this map, the watermark can be accurately synchronized, and then the extraction can be done. Experimental results demonstrate the excellent robustness of the proposed watermarking scheme to local geometric distortions, global geometric distortions, common image processing operations, and some kinds of combined attacks.

Cover-Lossless Robust Image Watermarking Against Geometric Deformations.

Cover-lossless robust watermarking is a new research issue in the information hiding community, which can restore the cover image completely in case of no attacks. Most countermeasures proposed in the literature usually focus on additive noise-like manipulations such as JPEG compression, low-pass filtering and Gaussian additive noise, but few are resistant to challenging geometric deformations such as rotation and scaling. The main reason is that in the existing cover-lossless robust watermarking algorithms, those exploited robust features are related to the pixel position. In this article, we present a new cover-lossless robust image watermarking method by efficiently embedding a watermark into low-order Zernike moments and reversibly hiding the distortion due to the robust watermark as the compensation information for restoration of the cover image. The amplitude of the exploited low-order Zernike moments are: 1) mathematically invariant to scaling the size of an image and rotation with any angle; and 2) robust to interpolation errors during geometric transformations, and those common image processing operations. To reduce the compensation information, the robust watermarking process is elaborately and luminously designed by using the quantized error, the watermarked error and the rounded error to represent the difference between the original and the robust watermarked image. As a result, a cover-lossless robust watermarking system against geometric deformations is achieved with good performance. Experimental results show that the proposed robust watermarking method can effectively reduce the compensation information, and the new cover-lossless robust watermarking system provides strong robustness to those content-preserving manipulations including scaling, rotation, JPEG compression and other noise-like manipulations. In case of no attacks, the cover image can be recovered without any loss.

Segmentation of Lath-Like Structures via Localized Identification of Directionality in a Complex-Phase Steel

In this work, a segmentation approach based on analyzing local orientations and directions in an image, in order to distinguish lath-like from granular structures, is presented. It is based on common image processing operations. A window of appropriate size slides over the image, and the gradient direction and its magnitude inside this window are determined for each pixel. The histogram of all possible directions yields the main direction and its directionality. These two parameters enable the extraction of window positions which represent lath-like structures, and procedures to join these positions are developed. The usability of this approach is demonstrated by distinguishing lath-like bainite from granular bainite in so-called complex-phase steels, a segmentation task for which automated procedures are not yet reported. The segmentation results are in accordance with the regions recognized by human experts. The approach’s main advantages are its use on small sets of images, the easy access to the segmentation process and therefore a targeted adjustment of parameters to achieve the best possible segmentation result. Thus, it is distinct from segmentation using deep learning which is becoming more and more popular and is a promising solution for complex segmentation tasks, but requires large image sets for training and is difficult to interpret.

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.

High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska

It is well known that microtopography associated with ice wedge polygons drives pronounced, meter-scale spatial gradients in hydrologic and ecological processes on the tundra. However, high-resolution maps of polygonal geomorphology are rarely available, due to the complexity and subtlety of ice wedge polygon relief at landscape scales. Here we present a sub-meter resolution map of >106 discrete ice wedge polygons across a ~1200 km2 landscape, delineated within a lidar-derived digital elevation model. The delineation procedure relies on a convolutional neural network paired with a set of common image processing operations and permits explicit measurement of relative elevation at the center of each ice wedge polygon. The resulting map visualizes meter- to kilometer-scale spatial gradients in polygonal geomorphology across an extensive landscape with unprecedented detail. This high-resolution inventory of polygonal geomorphology provides rich spatial context for extrapolating observations of environmental processes across the landscape. The map also represents an extensive baseline dataset for quantifying contemporary land surface deformation (i.e., thermokarst) at the survey area, through future topographic surveys.

Gradient-Based Illumination Description for Image Forgery Detection

The goal of blind image forensics is to determine authenticity and origin of an image without using an explicitly embedded security scheme. Most existing forensic methods can roughly be grouped into statistical and physics-based approaches. Statistical methods can oftentimes be fully automated, and achieve impressive results on current state-of-the-art benchmarks. Physics-based methods explain image inconsistencies using an analytic model, and are more robust to common image processing operations such as resizing or recompression. In this work, we propose a physics-based forensic descriptor to characterize 2-D lighting environments of objects. The key idea is that the integral over a gradient field of an object indicates the direction of incident light in the image plane. In contrast to prior 2-D lighting methods, the proposed method is remarkably robust to changes in object color and variations in user input, as it operates on the whole object area instead of object contours. Furthermore, we show that the proposed method is unaffected by image resizing or compression, which makes it possible to analyze images that are impossible to analyze with current state-of-the-art statistical methods.

Hydra image processor: 5-D GPU image analysis library with MATLAB and python wrappers.

Light microscopes can now capture data in five dimensions at very high frame rates producing terabytes of data per experiment. Five-dimensional data has three spatial dimensions (x, y, z), multiple channels (λ) and time (t). Current tools are prohibitively time consuming and do not efficiently utilize available hardware. The hydra image processor (HIP) is a new library providing hardware-accelerated image processing accessible from interpreted languages including MATLAB and Python. HIP automatically distributes data/computation across system and video RAM allowing hardware-accelerated processing of arbitrarily large images. HIP also partitions compute tasks optimally across multiple GPUs. HIP includes a new kernel renormalization reducing boundary effects associated with widely used padding approaches. HIP is free and open source software released under the BSD 3-Clause License. Source code and compiled binary files will be maintained on http://www.hydraimageprocessor.com. A comprehensive description of all MATLAB and Python interfaces and user documents are provided. HIP includes GPU-accelerated support for most common image processing operations in 2-D and 3-D and is easily extensible. HIP uses the NVIDIA CUDA interface to access the GPU. CUDA is well supported on Windows and Linux with macOS support in the future.

Color Image Watermarking with Watermark Authentication against False Positive Detection Using SVD

With growing need of exchanging digital information, digital watermarking has been an important tool to help content creators prove ownership if there is any dispute arising due to copyrights infringement. Robustness, imperceptibility and security are most imperative attributes for any efficient watermarking scheme. This requires the embedded watermark to be resilient and retrievable even if the content undergoes one or more signal processing operations, in part or as a whole. Some common image processing operations to be tackled are compression, noise addition, rotation, filtering, geometric distortions, scaling, etc. This paper presents a color image watermarking technique based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) to eliminate false positive detection of watermark. In this paper, either of the two copies of digital signature of the Eigen-vectors of the claimed watermark is used to authenticate itself before using it for extraction. The color image is represented in Hue, Saturation and Value (HSV) model. The watermark and its signature is inserted in 1-level and 4-level DWT coefficients respectively, of the value component of the colored image. Peak Signal to Noise Ratio (PSNR) of 55.3968 dB is achieved for the color Lena image and the watermark image is recovered with Correlation Coefficient (CC) as 0.9197.

A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins

The histogram watermark, which performs watermark embedding by slightly modifying the histogram of the original image, has been a hot research topic in information hiding technology due to the superiority of its pixel modification during the watermark embedding process, which is independent of the pixel position. This property makes the histogram-based watermark strong resistant to geometric attacks, such as cropping attack, crossed attack, rotation attack, etc. In this paper, we propose a large capacity histogram-based robust watermarking algorithm based on three consecutive bins for the first time. In our scheme, we divide the shape of three consecutive bins into eight cases. According to these cases, we embed Information Number 0, 1, 2, 3, 4, 5, 6, or 7, respectively. The embedded information capacity reaches one bit per bin (bpb), and the amount of embedded information is equal to 200% of the previous existing algorithms. Experimental results show that the new algorithm not only has a large capacity of embedding information, but also has strong robustness to geometric attacks, as well as common image processing operations.

Transform‐based watermarking algorithm maintaining perceptual transparency

Here, a novel, yet simple, transform-based watermarking scheme for copyright protection has been proposed. The proposed method takes advantage of the rotation invariant property of the ripplet-II transform which makes the proposed method robust against rotation attacks. In the proposed method, firstly a cost function of perceptual transparency is formed in the ripplet-II domain. Secondly, the ridge regularisation constraint is added to the cost function to avoid the singularity problem in the model. In order to obtain the embedding weights, this function is minimised. Therefore, the embedding weights are adaptive to both the host image and watermark. Simulation results demonstrate that the proposed algorithm not only provides an excellent watermark invisibility, but it also shows superior robustness against common image processing operations and acceptable robustness against geometric distortions.

More than Fifty Shades of Grey: Quantitative Characterization of Defects and Interpretation Using SNR and CNR

The quantitative characterization of defects in images is commonly performed using the signal-to-noise ratio (SNR). However, there is a strong debate about this measure. First, because there is no single accepted definition of SNR. Second, because the SNR measurements are highly affected by the regions used to estimate the power of the signal and noise in the image. This work provides an overview of some of the most commonly used SNR measures. Images with different sources of noise, and defects with different contrasts, are used to evaluate and compare the ability of these measures to quantitatively characterize defects. The measures are also evaluated when the images are transformed using common image processing operations, including filtering and gamma correction. This work also proposes a methodology to define the regions used to estimate the power of the signal and noise in the images. Two alternative procedures are proposed weather prior information is available about the inspected specimen or not. The proposed methodology is applied on real data from infrared testing, where the considered SNR measures are evaluated.

Selective bit embedding scheme for robust blind color image watermarking

In this paper, we propose a novel robust blind color image watermarking method, namely SMLE, that allows to embed a gray-scale image as watermark into a host color image in the wavelet domain. After decomposing the gray-scale watermark to component binary images in digits ordering from least significant bit (LSB) to most significant bit (MSB), the retrieved binary bits are then embedded into wavelet blocks of two optimal color channels by using an efficient quantization technique, where the wavelet coefficient difference in each block is quantized to either two pre-defined thresholds for corresponding 0-bits and 1-bits. To optimize the watermark imperceptibility, we equally split the coefficient modified quantity on two middle-frequency sub-bands instead of only one as in existing approaches. The improvement of embedding rule increases approximately 3 dB of watermarked image quality. An adequate trade-off between robustness and imperceptibility is controlled by a factor representing the embedding strength. As for extraction process, we exploit 2D Otsu algorithm for higher accuracy of watermark detection than that of 1D Otsu. Experimental results prove the robustness of our SMLE watermarking model against common image processing operations along with its efficient retention of the imperceptibility of the watermark in the host image. Compared to state-of-the-art methods, our approach outperforms in most of robustness tests at a same high payload capacity.

Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images

With the popularity of network and the continuous development of multimedia technology, saving of network bandwidth and copyright protection of multimedia content have gradually attracted people’s attention. The fragile watermark for integrity authentication of image data and protection of copyright has become a hotspot. In the storage and transmission process, image data must be compressed to save network bandwidth. As a result, semi-fragile watermarking techniques, which can be used to distinguish common image processing operations from malicious tampering, are emerging. In this paper, semi-fragile watermarking algorithms for image authentication are surveyed. The basic principles and characteristics about semi-fragile watermarking algorithms are introduced, and several kinds of attack behaviors are also included. Aiming at several typical image-authentication algorithms, advantages and disadvantages are analyzed, and evaluation indexes of various algorithms are compared. Finally, we analyze the key points and difficulties in the study on semi-fragile watermarking algorithms, and the direction about future development is prospected.

A robust and lossless DNA encryption scheme for color images

In this paper, a new robust and lossless color image encryption algorithm is presented based on DNA sequence operation and one-way coupled-map lattices (OCML). The plain-image is firstly decomposed into three gray-level components and we randomly convert them into three DNA matrices by the DNA encoding rules. Then the XOR operation is performed on the DNA matrices for two times. Next, the shuffled DNA matrices are transformed into three gray images according to the DNA decoding rules. Finally, a diffusion process is further applied to change the image pixel’s values by a key stream, and the cipher-image is attained. The key stream generated by OCML is related to the plain-image. Experimental results and security analysis demonstrate that the proposed algorithm has a good encryption effect and can withstand various typical attacks. Furthermore, it is robust against some common image processing operations such as noise adding, cropping, JPEG compression etc.

A robust color image watermarking using local invariant significant bitplane histogram

Desynchronization attacks 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 desynchronization attacks, especially for color images. In this paper, we propose a robust color image watermarking approach based on local invariant significant bitplane histogram. The novelty of the proposed approach includes: 1) A fast and effective color image feature points detector is constructed, in which probability density and color invariance model are used; 2) The fully affine invariant local feature regions are built based on probability density Hessian matrix; and 3) The invariant significant bitplane histograms are introduced to embed digital watermark. The extensive experimental works are carried out on a color image set collected from Internet, and the preliminary results show that the proposed watermarking approach can survive numerous kinds of distortions, including common image processing operations and desynchronization attacks.

A QDCT- and SVD-based color image watermarking scheme using an optimized encrypted binary computer-generated hologram

Based on quaternion discrete cosine transform (QDCT) and singular value decomposition (SVD), a novel color image watermarking scheme using an optimized encrypted binary computer-generated hologram (CGH) as the watermark is presented. A Fibonacci transform-based binary CGH technique that uses particle swarm optimization (PSO) algorithm to improve the reconstructed image quality has been proposed to generate a hologram of a watermark. By use of QDCT, the host color image is treated holistically as a vector field. The component of the host color image after QDCT, which is suitable for embedding watermark, is analyzed. With SVD, the mark CGH is inserted into the host color image by modifying the U matrix. To embed the binary CGH, multiple embedding strength factors are used in this study. To withstand the geometric attacks, based on Zernike moment algorithm and invariant centroid, an improved geometric distortion detection algorithm is proposed. Considering the significant color information, this algorithm is performed on the color invariance model. To obtain better imperceptibility and robustness, PSO is used for finding the proper multiple embedding strength factors. Compared with traditional geometric estimation method, the improved algorithm can estimate the transform parameters of the geometric distorted color image more accurately. Experimental results show that the proposed color image watermarking is not only secure and invisible, but also robust against common image processing operations and geometrical distortions.

Local quaternion PHT based robust color image watermarking algorithm

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.