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So far, researchers have proposed many forensics tools to protect the authenticity and integrity of digital information. However, with the explosive development of machine learning, existing forensics tools may compromise against new attacks anytime. Hence, it is always necessary to investigate anti-forensics to expose the vulnerabilities of forensics tools. It is beneficial for forensics researchers to develop new tools as countermeasures. To date, one of the potential threats is the generative adversarial networks (GANs), which could be employed for fabricating or forging falsified data to attack forensics detectors. In this article, we investigate the anti-forensics performance of GANs by proposing a novel model, the ExS-GAN, which features an extra supervision system. After training, the proposed model could launch anti-forensics attacks on various manipulated images. Evaluated by experiments, the proposed method could achieve high anti-forensics performance while preserving satisfying image quality. We also justify the proposed extra supervision via an ablation study.

Real-time semantic segmentation is widely used in autonomous driving and robotics. Most previous networks achieved great accuracy based on a complicated model involving mass computing. The existing lightweight networks generally reduce the parameter sizes by sacrificing the segmentation accuracy. It is critical to balance the parameters and accuracy for real-time semantic segmentation. In this article, we propose a lightweight multiscale-feature-fusion network (LMFFNet) mainly composed of three types of components: split-extract-merge bottleneck (SEM-B) block, feature fusion module (FFM), and multiscale attention decoder (MAD), where the SEM-B block extracts sufficient features with fewer parameters. FFMs fuse multiscale semantic features to effectively improve the segmentation accuracy and the MAD well recovers the details of the input images through the attention mechanism. Without pretraining, LMFFNet-3-8 achieves 75.1% mean intersection over union (mIoU) with 1.4 M parameters at 118.9 frames/s using RTX 3090 GPU. More experiments are investigated extensively on various resolutions on other three datasets of CamVid, KITTI, and WildDash2. The experiments verify that the proposed LMFFNet model makes a decent tradeoff between segmentation accuracy and inference speed for real-time tasks. The source code is publicly available at https://github.com/Greak-1124/LMFFNet.

As one of the most important topics in image forensics, resampling detection has developed rapidly in recent years. However, the robustness to JPEG compression is still challenging for most classical spectrum-based methods, since JPEG compression severely degrades the image contents and introduces block artifacts in the boundary of the compression grid. In this article, we propose a method to estimate the upscaling factors on double JPEG compressed images in the presence of image upscaling between the two compressions. We first analyze the spectrum of scaled images and give an overall formulation of how the scaling factors along with the parameters of JPEG compression and image contents influence the appearance of tampering artifacts. The expected positions of five kinds of characteristic peaks are analytically derived. Then, we analyze the features of double JPEG compressed images in the block discrete cosine transform (BDCT) domain and present an inverse scaling strategy for the upscaling factor estimation with a detailed proof. Finally, a fusion method is proposed that through frequency-domain analysis, a candidate set of upscaling factors is given, and through analysis in the BDCT domain, the optimal estimation from all candidates is determined. The experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.

In convolutional neural networks (CNNs), generating noise for the intermediate feature is a hot research topic in improving generalization. The existing methods usually regularize the CNNs by producing multiplicative noise (regularization weights), called multiplicative regularization (Multi-Reg). However, Multi-Reg methods usually focus on improving generalization but fail to jointly consider optimization, leading to unstable learning with slow convergence. Moreover, Multi-Reg methods are not flexible enough since the regularization weights are generated from a definite manual-design distribution. Besides, most popular methods are not universal enough, because these methods are only designed for the residual networks. In this article, we, for the first time, experimentally and theoretically explore the nature of generating noise in the intermediate features for popular CNNs. We demonstrate that injecting noise in the feature space can be transformed to generating noise in the input space, and these methods regularize the networks in a Mini-batch in Mini-batch (MiM) sampling manner. Based on these observations, this article further discovers that generating multiplicative noise can easily degenerate the optimization due to its high dependence on the intermediate feature. Based on these studies, we propose a novel additional regularization (Addi-Reg) method, which can adaptively produce additional noise with low dependence on intermediate feature in CNNs by employing a series of mechanisms. Particularly, these well-designed mechanisms can stabilize the learning process in training, and our Addi-Reg method can pertinently learn the noise distributions for every layer in CNNs. Extensive experiments demonstrate that the proposed Addi-Reg method is more flexible and universal, and meanwhile achieves better generalization performance with faster convergence against the state-of-the-art Multi-Reg methods.

Ball milling is a widely used method to produce graphene and other two-dimensional (2D) materials for both industry and research. Conventional ball milling generates strong impact forces, producing small and thick nanosheets that limit their applications. In this study, a viscous solvent-assisted planetary ball milling method has been developed to produce large thin 2D nanosheets. The viscous solvent simultaneously increases the exfoliation energy ( E e ) and lowers the impact energy ( E i ). Simulations show a giant ratio of η = E e / E i , for the viscous solvent, 2 orders of magnitude larger than that of water. The method provides both a high exfoliation yield of 74%, a high aspect ratio of the generated nanosheets of 571, and a high quality for a representative 2D material of boron nitride nanosheets (BNNSs). The large thin BNNSs can be assembled into high-performance functional films, such as separation membranes and thermally conductive flexible films with some performance parameters better than those 2D nanosheets produced by chemical exfoliation methods.

Syndrome-trellis codes (STCs) are commonly used in image steganographic schemes, which aim at minimizing the embedding distortion, but most distortion models cannot capture the mutual interaction of embedding modifications (MIEMs). In this article, a secure halftone image steganographic scheme based on a feature space and layer embedding is proposed. First, a feature space is constructed by a characterization method that is designed based on the statistics of 4 ×4 pixel blocks in halftone images. Upon the feature space, a generalized steganalyzer with good classification ability is proposed, which is used to measure the embedding distortion. As a result, a distortion model based on a hybrid feature space is constructed, which outperforms some state-of-the-art models. Then, as the distortion model is established on the statistics of local regions, a layer embedding strategy is proposed to reduce MIEM. It divides the host image into multiple layers according to their relative positions in 4 ×4 blocks, and the embedding procedure is executed layer by layer. In each layer, any two pixels are located at different 4 ×4 blocks in the original image, and the distortion model makes sure that the calculation of pixel distortions is independent. Between layers, the pixel distortions of the current layer are updated according to the previous embedding modifications, thus reducing the total embedding distortion. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high statistical security when resisting the state-of-the-art steganalysis.

The harmonic neural network (HNN) learns a combination of discrete cosine transform (DCT) filters to obtain an integrated feature from all spectra in the frequency domain. HNN, however, faces two challenges in learning and inference processes. First, the spectrum feature learned by HNN is insufficient and limited because the number of DCT filters is much smaller than that of feature maps. In addition, the number of parameters and the computation costs of HNN are significantly high because the intermediate spectrum layers are expanded multiple times. These two challenges will severely harm the performance and efficiency of HNN. To solve these problems, we first propose the compound DCT (C-DCT) filters integrating the nearest DCT filters to retrieve rich spectrum features to improve the performance. To significantly reduce the model size and computation complexity for improving the efficiency, the shared reconstruction filter is then proposed to share and dynamically drop the meta-filters in every frequency branch. Integrating the C-DCT filters with the shared reconstruction filters, the efficient harmonic network (EH-Net) is introduced. Extensive experiments on different datasets demonstrate that the proposed EH-Nets can effectively reduce the model size and computation complexity while maintaining the model performance. The code has been released at https://github.com/zhangle408/EH-Nets.

Changes in aerosol composition and its proportions can cause changes in atmospheric visibility. Vision systems deployed outdoors must take into account the negative effects brought by visibility impairment. In order to develop vision algorithms that can adapt to low atmospheric visibility conditions, a large-scale dataset containing pairs of clear images and their visibility-impaired versions (along with other annotations if necessary) is usually indispensable. However, it is almost impossible to collect large amounts of such image pairs in a real physical environment. A natural and reasonable solution is to use virtual simulation technologies, which is also the focus of this paper. In this paper, we first deeply analyze the limitations and irrationalities of the existing work specializing on simulation of atmospheric visibility impairment. We point out that many simulation schemes actually even violate the assumptions of the Koschmieder's law. Second, more importantly, based on a thorough investigation of the relevant studies in the field of atmospheric science, we present simulation strategies for five most commonly encountered visibility impairment phenomena, including mist, fog, natural haze, smog, and Asian dust. Our work establishes a direct link between the fields of atmospheric science and computer vision. In addition, as a byproduct, with the proposed simulation schemes, a large-scale synthetic dataset is established, comprising 40,000 clear source images and their 800,000 visibility-impaired versions. To make our work reproducible, source codes and the dataset have been released at https://cslinzhang.github.io/AVID/.

Accurate sintering condition recognition (SCR) is an important precondition for optimal control of rotary kilns. However, the occurrence probability of abnormal conditions in the industrial field is much lower than normal, resulting in imbalanced class sintering samples in general. This significantly deteriorates the effectiveness of existing recognition models in abnormal condition detection. In this paper, an integrated framework considering class imbalance is proposed for sintering condition recognition. In the proposed framework, after analysing the characteristics of thermal signals by the Lipschitz method, four discriminant features are extracted to comprehensively describe different sintering conditions. In addition, focusing on the class imbalance of sintering samples, the kernel modification method is introduced to enhance the optimal marginal distribution machine (ODM), and a novel recognition model kernel modified the ODM (KMODM) is proposed for SCR. By constructing a new conformal transformation function to modify the ODM kernel function, KMODM optimizes the spatial distribution of training samples in the kernel space, thereby alleviating the detection accuracy deterioration of the minority class. The experimental results on real thermal signals and standard datasets show that the KMODM model can effectively handle imbalanced data. Based on this, the proposed SCR framework can reduce the misjudgement of abnormal conditions and balance the recognition accuracy of each condition., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

Collaborative representation is an effective way to design classifiers for many practical applications. In this paper, we propose a novel classifier, called the prior knowledge-based probabilistic collaborative representation-based classifier (PKPCRC), for visual recognition. Compared with existing classifiers which use the collaborative representation strategy, the proposed PKPCRC further includes characteristics of training samples of each class as prior knowledge. Four types of prior knowledge are developed from the perspectives of image distance and representation capacity. They adaptively accommodate the contribution of each class and result in an accurate representation to classify a query sample. Experiments and comparisons on four challenging databases demonstrate that PKPCRC outperforms several state-of-the-art classifiers.

Many existing non-local means (NLM) methods either use Euclidean distance to measure the similarity between patches, or compute weight ω ij only once and keep it unchanged during the subsequent denoising iterations, or use only the structure information of the denoised image to update weight ω ij . These may lead to the limited denoising performance. To address these issues, this paper proposes the non-local adaptive means (NLAM) for image denoising. NLAM treats weight ω ij as an optimization variable and iteratively updates its value. We then introduce three unbiased distances, namely, pixel-pixel, patch-patch, and coupled unbiased distances. These unbiased distances are more robust to measure the image pixel/patch similarity than Euclidean distance. Using the coupled unbiased distance, we propose the unbiased distance non-local adaptive means (UD-NLAM). Because UD-NLAM uses only a single patch size to compute weight ω ij , we introduce multipatch UD-NLAM (MUD-NLAM) to adapt different noise levels. To further improve denoising performance, we then propose a new denoising method called MUD-NLAM with wavelet shrinkage (MUD-NLAM-WS). Experimental results show that the proposed NLAM, UD-NLAM, and MUD-NLAM outperform existing NLM methods, and MUD-NLAM-WS achieves a better performance than the state-of-the-art denoising methods.

Linear discriminant analysis has been incorporated with various representations and measurements for dimension reduction and feature extraction. In this paper, we propose two-dimensional quaternion sparse discriminant analysis (2D-QSDA) that meets the requirements of representing RGB and RGB-D images. 2D-QSDA advances in three aspects: 1) including sparse regularization, 2D-QSDA relies only on the important variables, and thus shows good generalization ability to the out-of-sample data which are unseen during the training phase; 2) benefited from quaternion representation, 2D-QSDA well preserves the high order correlation among different image channels and provides a unified approach to extract features from RGB and RGB-D images; 3) the spatial structure of the input images is retained via the matrix-based processing. We tackle the constrained trace ratio problem of 2D-QSDA by solving a corresponding constrained trace difference problem, which is then transformed into a quaternion sparse regression (QSR) model. Afterward, we reformulate the QSR model to an equivalent complex form to avoid the processing of the complicated structure of quaternions. A nested iterative algorithm is designed to learn the solution of 2D-QSDA in the complex space and then we convert this solution back to the quaternion domain. To improve the separability of 2D-QSDA, we further propose 2D-QSDAw using the weighted pairwise between-class distances. Extensive experiments on RGB and RGB-D databases demonstrate the effectiveness of 2D-QSDA and 2D-QSDAw compared with peer competitors.

Low-rank matrix approximation (LRMA)-based methods have made a great success for grayscale image processing. When handling color images, LRMA either restores each color channel independently using the monochromatic model or processes the concatenation of three color channels using the concatenation model. However, these two schemes may not make full use of the high correlation among RGB channels. To address this issue, we propose a novel low-rank quaternion approximation (LRQA) model. It contains two major components: first, instead of modeling a color image pixel as a scalar in conventional sparse representation and LRMA-based methods, the color image is encoded as a pure quaternion matrix, such that the cross-channel correlation of color channels can be well exploited; second, LRQA imposes the low-rank constraint on the constructed quaternion matrix. To better estimate the singular values of the underlying low-rank quaternion matrix from its noisy observation, a general model for LRQA is proposed based on several nonconvex functions. Extensive evaluations for color image denoising and inpainting tasks verify that LRQA achieves better performance over several state-of-the-art sparse representation and LRMA-based methods in terms of both quantitative metrics and visual quality.

The explosive growth of multimedia data on the Internet makes it essential to develop innovative machine learning algorithms for practical applications especially where only a small number of labeled samples are available. Manifold regularized semi-supervised learning (MRSSL) thus received intensive attention recently because it successfully exploits the local structure of data distribution including both labeled and unlabeled samples to leverage the generalization ability of a learning model. Although there are many representative works in MRSSL, including Laplacian regularization (LapR) and Hessian regularization, how to explore and exploit the local geometry of data manifold is still a challenging problem. In this paper, we introduce a fully efficient approximation algorithm of graph p -Laplacian, which significantly saving the computing cost. And then we propose p -LapR (pLapR) to preserve the local geometry. Specifically, p -Laplacian is a natural generalization of the standard graph Laplacian and provides convincing theoretical evidence to better preserve the local structure. We apply pLapR to support vector machines and kernel least squares and conduct the implementations for scene recognition. Extensive experiments on the Scene 67 dataset, Scene 15 dataset, and UC-Merced dataset validate the effectiveness of pLapR in comparison to the conventional manifold regularization methods.

Benefited from quaternion representation that is able to encode the cross-channel correlation of color images, quaternion principle component analysis (QPCA) was proposed to extract features from color images while reducing the feature dimension. A quaternion covariance matrix (QCM) of input samples was constructed, and its eigenvectors were derived to find the solution of QPCA. However, eigen-decomposition leads to the fixed solution for the same input. This solution is susceptible to outliers and cannot be further optimized. To solve this problem, this paper proposes a novel quaternion ridge regression (QRR) model for two-dimensional QPCA (2D-QPCA). We mathematically prove that this QRR model is equivalent to the QCM model of 2D-QPCA. The QRR model is a general framework and is flexible to combine 2D-QPCA with other technologies or constraints to adapt different requirements of real-world applications. Including sparsity constraints, we then propose a quaternion sparse regression model for 2D-QSPCA to improve its robustness for classification. An alternating minimization algorithm is developed to iteratively learn the solution of 2D-QSPCA in the equivalent complex domain. In addition, 2D-QPCA and 2D-QSPCA can preserve the spatial structure of color images and have a low computation cost. Experiments on several challenging databases demonstrate that 2D-QPCA and 2D-QSPCA are effective in color face recognition, and 2D-QSPCA outperforms the state of the arts.

Recent studies have shown the effectiveness of using depth information in salient object detection. However, the most commonly seen images so far are still RGB images that do not contain the depth data. Meanwhile, the human brain can extract the geometric model of a scene from an RGB-only image and hence provides a 3D perception of the scene. Inspired by this observation, we propose a new concept named RGB-'D' saliency detection, which derives pseudo depth from the RGB images and then performs 3D saliency detection. The pseudo depth can be utilized as image features, prior knowledge, an additional image channel, or independent depth-induced models to boost the performance of traditional RGB saliency models. As an illustration, we develop a new salient object detection algorithm that uses the pseudo depth to derive a depth-driven background prior and a depth contrast feature. Extensive experiments on several standard databases validate the promising performance of the proposed algorithm. In addition, we also adapt two supervised RGB saliency models to our RGB-'D' saliency framework for performance enhancement. The results further demonstrate the generalization ability of the proposed RGB-'D' saliency framework.

Ultrashort channels of electrodes are essential for the construction of advanced functional devices with high-level integration and high operation speed. However, the channel length of fabricated electrodes is limited to 20 μm in inkjet printing. Although several methods have been previously proposed to obtain short channels, they require extra processing steps. In this paper, channel self-aligning phenomenon was observed in directly patterned electrodes on unmodified substrate by inkjet printing, when using an interspace defects growing method. Further exploring the underlying mechanism reveals that the capillary force induced air film prevents droplets coalescence, even on a substrate with no temperature differences. The wetting region, which is generated by the receding droplets impingement, will draw droplets closer together at a larger drop space, thus demanding smaller air pressure for coalescence inhibition and contributing to the self-aligning phenomenon of micro-sized droplets released by inkjet printing. Accordingly, an ultrashort channel of 2.38 μm is obtained with relatively smooth boundaries, when electrodes are printed on a slightly heated substrate, which reduces the air pressure between two neighboring droplets. This work will provide a significant reference for future high resolution applications of inkjet printing technology.

We first verify the critical role of solvent evaporation on the resolution of inkjet printing. To confirm our hypothesis, we adjusted the evaporation rate gradient along the surface of adjacent droplets by controlling the drying microenvironment. Uneven solvent evaporation flux caused thermocapillary surface flow inward the space of micrometer-sized droplets and increase the air pressure, which prevented the neighboring droplets from coalescence. When reducing the droplet distance by the solvent evaporation-based method, a uniform profile could be obtained at the same time. This work brings us a step closer to resolving one of the critical bottlenecks to commercializing printed electronic goods.

Superpixel segmentation targets at grouping pixels in an image into atomic regions whose boundaries align well with the natural object boundaries. This paper first proposes a new feature representation for superpixel segmentation that holistically embraces color, contour, texture, and spatial features. Then, we introduce a clustering-based discriminability measure to iteratively evaluate the importance of different features. Integrating the feature representation and the discriminability measure, we propose a novel content-adaptive superpixel (CAS) segmentation algorithm. CAS is able to automatically and iteratively adjust the weights of different features to fit various properties of image instances. Experiments on several challenging datasets demonstrate that the proposed CAS outperforms the state-of-the-art methods and has a low computational cost.

Generating chaotic maps with expected dynamics of users is a challenging topic. Utilizing the inherent relation between the Lyapunov exponents (LEs) of the Cat map and its associated Cat matrix, this paper proposes a simple but efficient method to construct an -dimensional ( -D) hyperchaotic Cat map (HCM) with any desired number of positive LEs. The method first generates two basic -D Cat matrices iteratively and then constructs the final -D Cat matrix by performing similarity transformation on one basic -D Cat matrix by the other. Given any number of positive LEs, it can generate an -D HCM with desired hyperchaotic complexity. Two illustrative examples of -D HCMs were constructed to show the effectiveness of the proposed method, and to verify the inherent relation between the LEs and Cat matrix. Theoretical analysis proves that the parameter space of the generated HCM is very large. Performance evaluations show that, compared with existing methods, the proposed method can construct -D HCMs with lower computation complexity and their outputs demonstrate strong randomness and complex ergodicity.

Inkjet-printed silver gate electrode with low electrical resistivity was fabricated by UV curing method. By adjusting the UV curing time and the distance between the samples and UV lamp, the effects of UV curing conditions on the electrical resistivity of the silver films were studied, and the lowest electrical resistivity of 6.69 × 10 -8 Ω·m was obtained. Besides, the UV-cured silver films have good adhesion to the glass substrates, with adhesion strength of 4B (ASTM international standard). Our work offered an easy and low temperature approach to fabricate inkjet-printed silver electrodes with low electrical resistivity.

The features used in many current medical image retrieval systems are usually low-level hand-crafted features. This limitation may adversely affect the retrieval performance. To address this problem, this paper proposes a simple yet discriminative feature, called histogram of compressed scattering coefficients (HCSC), for medical image retrieval. In the proposed work, the scattering transform, a particular variation of deep convolutional networks, is first performed to yield more abstract representations of a medical image. A projection operation is then conducted to compress the obtained scattering coefficients for efficient processing. Finally, a bag-of-words (BoW) histogram is derived from the compressed scattering coefficients as the features of the medical image. The proposed HCSC takes the advantages of both scattering transform and BoW model. Experiments on three benchmark medical computer tomography image databases demonstrate that HCSC outperforms several state-of-the-art features.

Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

This paper introduces a new parametric n-ary Gray code, the (n, k, p)-Gray code, which includes several commonly used codes such as the binary-reflected, ternary, and (n, k)-Gray codes. The new (n, k, p)-Gray code has potential applications in digital communications and signal/image processing systems. This paper focuses on three illustrative applications of the (n, k, p)-Gray code, namely, image bit-plane decomposition, image denoising, and encryption. The computer simulations demonstrate that the (n, k, p)-Gray code shows better performance than other traditional Gray codes for these applications in image systems.

Mammograms are widely used to detect breast cancer in women. The quality of the image may suffer from poor resolution or low contrast due to the limitations of the X-ray hardware systems. Image enhancement is a powerful tool to improve the visual quality of mammograms. This paper introduces a new powerful nonlinear filter called the alpha weighted quadratic filter for mammogram enhancement. The user has the flexibility to design the filter by selecting all of the parameters manually or using an existing quantitative measure to select the optimal enhancement parameters. Computer simulations show that excellent enhancement results can be obtained with no apriori knowledge of the mammogram contents. The filter can also be used for automatic segmentation.

Image encryption is an effective approach for providing security and privacy protection for medical images. This paper introduces a new lossless approach, called EdgeCrypt, to encrypt medical images using the information contained within an edge map. The algorithm can fully protect the selected objects/regions within medical images or the entire medical images. It can also encrypt other types of images such as grayscale images or color images. The algorithm can be used for privacy protection in the real-time medical applications such as wireless medical networking and mobile medical services.