Robust Scheme Research Articles

A Robust Mutual Authentication Scheme for Health-Focused Connected Intelligent Objects

A Robust Mutual Authentication Scheme for Health-Focused Connected Intelligent Objects

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

Optimal configuration of dynamic VAR compensators considering uncertainty and correlation

AbstractThe transient voltage stability of modern power systems is challenged by the increasing uncertainty on source‐load dual sides. To cope with it, a robust optimal configuration method for dynamic VAR compensators (DVCs) is proposed. First, a series of power‐flow scenarios are generated with uncertainty and correlation considered, where nonparametric kernel density estimation is used to predict the marginal distribution of wind speeds and combined Copula function is employed to characterize correlations between nearby wind farms. Then, the variation‐of‐information indicator is introduced to assess the similarity among power‐flow (PF) scenarios, and representative PF (RPF) scenarios are screened with the help of Spectral clustering algorithm. Finally, locating and sizing of DVCs for RPF scenarios are achieved. By synthesizing the compensation schemes for RPF scenarios, the robust configuration scheme is suggested. Simulation studies in the modified New England 10‐machine 39‐bus system show the proposed method can ensure transient voltage security of the studied power system under source‐load uncertainty.

Long-term stability in perovskite solar cells through atomic layer deposition of tin oxide.

Robust contact schemes that boost stability and simplify the production process are needed for perovskite solar cells (PSCs). We codeposited perovskite and hole-selective contact while protecting the perovskite to enable deposition of SnOx/Ag without the use of a fullerene. The SnOx, prepared through atomic layer deposition, serves as a durable inorganic electron transport layer. Tailoring the oxygen vacancy defects in the SnOx layer led to power conversion efficiencies (PCEs) of >25%. Our devices exhibit superior stability over conventional p-i-n PSCs, successfully meeting several benchmark stability tests. They retained >95% PCE after 2000 hours of continuous operation at their maximum power point under simulated AM1.5 illumination at 65°C. Additionally, they boast a certified T97 lifetime exceeding 1000 hours.

Robust cryptographic scheme for reliable data communication in VANET (RCSRC) using clustering approach

In contemporary times, as accident rates continue to rise, there is a growing interest in vehicular ad hoc networks (VANETs) as a means to reduce accidents and vehicle damage, as well as enhance public safety. VANETs facilitate the transmission of messages within predefined areas to ensure system safety and efficiency. However, ensuring message authenticity in such a dynamic environment presents a significant challenge, with previous research focusing less on security aspects. Some previous protocols send plain message, which leads to the compromise of the message confidentiality. Although few algorithms work on security, they involve the communication to the trusted authority, which lead to increased latency. Thus, in this paper we propose a robust cryptographic scheme for reliable data communication in VANET which minimizes the involvement of trusted authority and uses communication ID of vehicle for communication in place of real entities. Security analysis of the proposed scheme is showing the sturdiness of the proposed protocol again various types of safety spasms. The evaluation of its performance includes assessing communication cost and time, revealing the proposed algorithm's efficiency compared to others.

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

Robust model averaging approach by Mallows-type criterion.

Model averaging is an important tool for treating uncertainty from model selection process and fusing information from different models, and has been widely used in various fields. However, the most existing model averaging criteria are proposed based on the methods of ordinary least squares or maximum likelihood, which possess high sensitivity to outliers or violation of certain model assumption. For the mean regression, no optimal robust methods are developed. To fill this gap, in our paper, we propose an outlier-robust model averaging approach by Mallows-type criterion. The idea is that we first construct a generalized M (GM) estimator for each candidate model, and then build robust weighting schemes by the asymptotic expansion of the final prediction error based on the GM-type loss function. So, we can still achieve a trustworthy result even if the dataset is contaminated by outliers in response and/or covariates. Asymptotic properties of the proposed robust model averaging estimators are established under some regularity conditions. The consistency of our weight estimators tending to the theoretically optimal weight vectors is also derived. We prove that our model averaging estimator is robust in terms of having bounded influence function. Further, we define the empirical prediction influence function to evaluate the quantitative robustness of the model averaging estimator. A simulation study and a real data analysis are conducted to demonstrate the finite sample performance of our estimators and compare them with other commonly used model selection and averaging methods.

Chiral edge states and direct edge-to-edge transport in a bosonic magnetic ladder

Abstract Three-legged magnetic ladder reproduces the main features of two-dimensional Hofstadter butterfly spectrum and the related Chern insulating phases with typical topological edge states, providing new opportunity to study novel quantum states and chiral physics. Here, we propose an efficient scheme to detect various edge states and realize edge-to-edge transport in a three-legged bosonic magnetic ladder. Under the mean field approximation, the eigenstates, chiral edge-state dynamics, and edge-to-edge transport in the system are studied. The energy spectrum and the eigenstates of the system are presented, and both bulk and edge states are obtained, depending on the energy spectrum of the system. The existence of rich edge states (including symmetric and unsymmetric edge states) provides the evidence for realizing the topological transport in the system. Furthermore, chiral edge-state dynamics is excited by applying a weak linear external force, reproducing the underlying eigenstates dynamically, offering a robust way to detect the edge states of the system. Particularly, direct edge-to-edge transport is observed which can be identified by the observables (spin polarization, spin tensor, and chiral currents). The reversal of chiral currents induces the edge-to-edge transport. The edge-to-edge transport time can be manipulated by adjusting the external force and magnetic field. We provide a robust and efficient atomic transport scheme with potential applications in manipulating topological quantum transport and storing quantum states in the ultracold atomic system.

Novel Robust Video Watermarking Scheme Based on Concentric Ring Subband and Visual Cryptography With Piecewise Linear Chaotic Mapping

Novel Robust Video Watermarking Scheme Based on Concentric Ring Subband and Visual Cryptography With Piecewise Linear Chaotic Mapping

Adaptive robust watermarking scheme based on RIME optimization with integer wavelet transform, singular value decomposition, and Hadamard transform

Adaptive robust watermarking scheme based on RIME optimization with integer wavelet transform, singular value decomposition, and Hadamard transform

Enhancing medical image security through machine learning and dual watermarking-based technique

ABSTRACT As the world becomes increasingly digital, healthcare is no exception. With the ease of sharing e-healthcare records over open networks, smart healthcare systems have become a popular way to manage patient information. But as the popularity of these systems has grown, so has the concern for their security. That's where image security techniques come in. In this paper we have developed a new approach to secure e-patient records like DICOM images. By combining the redundant discrete wavelets transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD). We developed a robust and dual watermarking scheme. This scheme uses multiple watermarks, including Electronic Patient Record (EPR) as text and images, to ensure high-level authentication. In order to attain a balance between imperceptibility and robustness, a PSO-based optimization of scale factor is employed and Turbo code is utilized to encode the EPR and minimize channel noise. Additionally, the marked image undergoes encryption using a 3D chaotic-based encryption technique, and the extracted watermark is denoised through a deep neural network. The result shows that the proposed scheme is both secure and reliable. With this dual watermarking scheme, we have made great strides in securing e-patient records.

Extended B-Spline Collocation Method for Singularly Perturbed Time Delay Parabolic Differential-Difference Problems

The paper presents a robust collocation B-spline numerical scheme for solving singularly perturbed time lag parabolic differential–difference problems. The method uses Taylor’s series expansion for the approximation of retarded terms, the [Formula: see text]-method (when [Formula: see text]) for time discretization, and the extended third-degree B-spline collocation method for spatial discretization in a piecewise uniform Shishkin mesh. The advantage of using an extended cubic B-spline rather than the classical third-degree B-spline method is that it introduces one additional free parameter to control the global shape parameter. The stability and uniform convergence of the proposed scheme are investigated. The method is first-order accurate in [Formula: see text] and almost second-order accurate in [Formula: see text], surpassing existing literature methods.

Fast and robust JND-guided video watermarking scheme in spatial domain

Generally speaking, those watermarking studies using the spatial domain tend to be fast but with limited robustness and imperceptibility while those performed in other transform domains are robust but have high computational cost. Watermarking applied to digital video has as one of the main challenges the large amount of computational power required due to the huge amount of information to be processed. In this paper we propose a watermarking algorithm for digital video that addresses this problem. To increase the speed, the watermark is embedded using a technique to modify the DCT coefficients directly in the spatial domain, in addition to carrying out this process considering the video scene as the basic unit and not the video frame. In terms of robustness, the watermark is modulated by a Just Noticeable Distortion (JND) scheme computed directly in the spatial domain guided by visual attention to increase the strength of the watermark to the maximum level but without this operation being perceivable by human eyes. Experimental results confirm that the proposed method achieves remarkable performance in terms of processing time, robustness and imperceptibility compared to previous studies.

Image sterilization via overwriting

Sterilization refers to the method of removing hidden information from images, effectively eradicating malicious data embedded within digital media. Existing methods are hampered by dual limitations: insufficient resilience to cutting-edge information hidden methods, coupled with the challenge of image restoration in quality. In response to these challenges, this paper proposes a novel method composed of overwriting attack and diffusion optimization stages. Employing the concept of pixel destruction, the overwriting method conducts a more thorough attack on hidden information while preserving sufficient information for image reconstruction and further diffusion optimization. This strategy is not bound to any particular information hiding method, making it effective against a wide range of steganography and watermarking techniques by systematically disrupting every pixel in the image. Diffusion optimization makes full use of the generative capabilities of diffusion models trained on large datasets, enabling higher quality image enhancements for a variety of scene images.Our method stands out in comparison to state-of-the-art studies, as experimental outcomes demonstrate its capability to maintain superior image integrity while efficiently eliminating hidden information across various steganography and watermarking techniques. Notably, when confronted with deep robust watermarking schemes, our approach exhibits a significant improvement over preceding methods. For instance, the removal rate improves significantly from 39.42% to 87.78% with higher quality restoration when compared to SC-Net. Our method delivers superior outcomes in the task of image sterilization, while presents a new challenge to the robustness of image information hiding methods and contributes to network security.

Robust optical singularity detection enabled by spin-synchronized shearing interference

Singular beams carrying phase and polarization singularities have been extensively studied for applications in optical communication, quantum information processing, optical imaging, and other fields. However, developing a robust method to detect optical singularity that is insensitive to wavelength, polarization, and illumination position remains a significant challenge. In this study, we propose and demonstrate a robust optical singularity detection scheme based on spin-synchronized shearing interference (SSSI) using spin-dependent gradient phase modulation of a quarter-wave liquid–crystal polarization grating. Since the spin-dependent gradient phase modulation of the polarization grating is wavelength-independent, and the two circular bases of the incident field can be measured simultaneously, the proposed method is robust to wavelength and polarization. Additionally, we have verified that SSSI performs well in detecting multi-singularity cylindrical vector beam. This work provides a practical and straightforward scheme for robust optical singularity detection.

A multi-dimensional, robust, and cell-centered finite-volume scheme for the ideal MHD equations

We present a new multi-dimensional, robust, and cell-centered finite-volume scheme for the ideal MHD equations. This scheme relies on relaxation and splitting techniques and can be easily used at high order. A fully conservative version is not entropy satisfying but is observed experimentally to be more robust than standard constrained transport schemes at low plasma beta. At very low plasma beta and high Alfvén number, we have designed an entropy-satisfying version that is not conservative for the magnetic field but preserves admissible states and we switch locally a-priori between the two versions depending on the regime of plasma beta and Alfvén number. This strategy is robust in a wide range of standard MHD test cases, all performed at second order with a classic MUSCL-Hancock scheme.

Adaptive mesh extended cubic B-spline method for singularly perturbed delay Sobolev problems

The purpose of this paper is to develop a robust numerical scheme for a class of singularly perturbed delay Sobolev (pseudo-parabolic) problems that have wide application in various branches of mathematical physics and fluid mechanics.For the small perturbation parameter, the standard numerical schemes for the solution of these problems fail to resolve the boundary layer(s) and the oscillations occur near the boundary layer. Thus, in this paper to resolve the boundary layer(s), im-plicit Euler scheme for the time derivatives on uniform mesh and extended B-spline basis functions consisting of free parameter λ are presented for spatial variable on Bakhvalov type mesh. The stability and uniform convergence analyisis of the proposed method are established. The error estimation of the developed method is shown to be firts order accurate in time and second order accurate in space. Numerical exprementation is carried out to validate the applicability of the developednumerical method. The numerical results reveals that the computational result is in agreement with the theoretical estimations

Robust Hashing with Deep Features and Meixner Moments for Image Copy Detection

Copy detection is a key task of image copyright protection. Most robust hashing schemes do not make satisfied performance of image copy detection yet. To address this, a robust hashing scheme with deep features and Meixner moments is proposed for image copy detection. In the proposed hashing, global deep features are extracted by applying tensor Singular Value Decomposition (t-SVD) to the three-order tensor constructed in the DWT domain of the feature maps calculated by the pre-trained VGG16. Since the feature maps in the DWT domain are slightly disturbed by digital operations, the constructed three-order tensor is stable and thus the desirable robustness is guaranteed. Moreover, since t-SVD can decompose a three-order tensor into multiple low-dimensional matrices reflecting intrinsic structure, the global deep feature calculation from the low-dimensional matrices can provide good discrimination. Local features are calculated by the block-based Meixner moments. As the Meixner moments are resistant to geometric transformation and can efficiently discriminate various images, the use of the block-based Meixner moments can make discriminative and robust local features. Hash is ultimately determined by quantifying and combining global deep features and local features. The results of extensive experiments on open image datasets demonstrate that the proposed robust hashing outperforms some state-of-the-art robust hashing schemes in terms of classification and copy detection performances.

Escrow-free and efficient dynamic anonymous privacy-preserving batch verifiable authentication scheme for VANETs

To enhance road safety, Vehicular Ad-hoc Networks (VANETs) facilitate the exchange of safety-critical messages between smart vehicles and road traffic authorities. However, VANET’s wireless channels are prone to several attacks, such as replay or modification. Therefore, to protect the links, robust authentication and message integrity mechanisms are required. Previously, several robust authentication schemes have been devised. However, those designs often struggle with complex certificate management, the key escrow problem, and the necessity for secure channels to establish user keys. Additionally, prior methods rely on pseudonyms to ensure user privacy. To implement it, several pseudonyms are stored in the vehicle’s device, which burdens the device. To overcome these limitations, this study introduces an efficient and escrow-free dynamic anonymous authentication scheme tailored for VANETs. By utilizing the paradigm of certificate-based cryptography and fuzzy identity generation, the proposed design eliminates the limitations. Through rigorous security analysis, the proposed design’s effectiveness against various threats is demonstrated. Furthermore, a detailed performance analysis, including computational and communication cost comparisons, showcases the scheme’s feasibility for VANET deployment. An NS-3 simulation further confirms the suitability of the proposed scheme for real-world VANET communication scenarios.

A robust hybridizable discontinuous Galerkin scheme with harmonic averaging technique for steady state of real-world semiconductor devices

Solving real-world nonlinear semiconductor device problems modeled by the drift-diffusion equations coupled with the Poisson equation (also known as the Poisson-Nernst-Planck equations) necessitates an accurate and efficient numerical scheme which can avoid non-physical oscillations even for problems with extremely sharp doping profiles. In this paper, we propose a flexible and high-order hybridizable discontinuous Galerkin (HDG) scheme with harmonic averaging (HA) technique to tackle these challenges. The proposed HDG-HA scheme combines the robustness of finite volume Scharfetter-Gummel (FVSG) method with the high-order accuracy and hp-flexibility offered by the locally conservative HDG scheme. The coupled Poisson equation and two drift-diffusion equations are simultaneously solved by the Newton method. Indicators based on the gradient of net doping and solution variables are proposed to switch between cells with HA technique and high-order conventional HDG cells, utilizing the flexibility of HDG scheme. Numerical results suggest that the proposed scheme does not exhibit oscillations or convergence issues, even when applied to heavily doped and sharp PN-junctions. Devices with circular junctions and realistic doping profiles are simulated in two dimensions, qualifying this scheme for practical simulation of real-world semiconductor devices.