Atheer Alrammahi^1,2, Hedieh Sajedi^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 887-918, 2025, DOI:10.32604/cmc.2025.063866 - 09 June 2025

Abstract Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution, tampering, and copyright infringement. This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform (DWT), Redundant Discrete Wavelet Transform (RDWT), and Möbius Transformations (MT), with optimization of transformation parameters achieved via a Genetic Algorithm (GA). By combining frequency and spatial domain techniques, the proposed method significantly enhances both the imperceptibility and robustness of watermark embedding. The approach leverages DWT and RDWT for multi-resolution decomposition, enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks. RDWT,… More >

Lingyun Xiang¹, Hang Fu¹, Chunfang Yang^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 325-345, 2025, DOI:10.32604/cmc.2025.062834 - 09 June 2025

Abstract In the Visual Place Recognition (VPR) task, existing research has leveraged large-scale pre-trained models to improve the performance of place recognition. However, when there are significant environmental differences between query images and reference images, a large number of ineffective local features will interfere with the extraction of key landmark features, leading to the retrieval of visually similar but geographically different images. To address this perceptual aliasing problem caused by environmental condition changes, we propose a novel Visual Place Recognition method with Cross-Environment Robust Feature Enhancement (CerfeVPR). This method uses the GAN network to generate similar… More >

Yi-Chen Lee¹, Yann-Horng Lin², Wen-Jer Chang^2,*, Muhammad Shamrooz Aslam^3,*, Zi-Yao Lin²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1433-1461, 2025, DOI:10.32604/cmes.2025.064717 - 30 May 2025

Abstract An optimal fuzzy tracking synthesis for nonlinear discrete-time descriptor systems is discussed through the Parallel Distributed Compensation (PDC) approach and the Proportional-Difference (P-D) feedback framework. Based on the Takagi-Sugeno Fuzzy Descriptor Model (T-SFDM), a nonlinear discrete-time descriptor system is represented as several linear fuzzy subsystems, which facilitates the linear P-D feedback technique and streamlines the fuzzy controller design process. Leveraging the P-D feedback fuzzy controller, the closed-loop T-SFDM can be transformed into a standard system that guarantees non-impulsiveness and causality for the nonlinear discrete-time descriptor system. In view of the disturbance problems, a passive performance… More >

Zhengdao Yang¹, Xuewei Wang², Yuling Chen^1,*, Hui Dou¹, Haiwei Sang³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5181-5197, 2025, DOI:10.32604/cmc.2025.060564 - 19 May 2025

Abstract Anomaly detection (AD) in time series data is widely applied across various industries for monitoring and security applications, emerging as a key research focus within the field of deep learning. While many methods based on different normality assumptions perform well in specific scenarios, they often neglected the overall normality issue. Some feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection, leading to decreased performance. Additionally, real-world time series samples are rarely free from noise, making them susceptible to outliers, which further impacts detection accuracy. To address these More >

Muath AlShaikh^*

Computer Systems Science and Engineering, Vol.49, pp. 435-453, 2025, DOI:10.32604/csse.2025.062432 - 13 May 2025

Abstract Many existing watermarking approaches aim to provide a Robust Reversible Data Hiding (RRDH) method. However, most of these approaches degrade under geometric and non-geometric attacks. This paper presents a novel RRDH approach using Polar Harmonic Fourier Moments (PHFMs) and linear interpolation. The primary objective is to enhance the robustness of the embedded watermark and improve the imperceptibility of the watermarked image. The proposed method leverages the high-fidelity and anti-geometric transformation properties of PHFMs. The image is transformed into the frequency domain of RRDH, after which compensation data is embedded using a two-dimensional RDH scheme. Linear… More >

Xuezhi Wen¹, Eric Danso^2,*, Solomon Danso²

Journal of Cyber Security, Vol.7, pp. 45-69, 2025, DOI:10.32604/jcs.2025.063606 - 08 May 2025

Abstract Deep learning models have achieved remarkable success in healthcare, finance, and autonomous systems, yet their security vulnerabilities to adversarial attacks remain a critical challenge. This paper presents a novel dual-phase defense framework that combines progressive adversarial training with dynamic runtime protection to address evolving threats. Our approach introduces three key innovations: multi-stage adversarial training with TRADES (Tradeoff-inspired Adversarial Defense via Surrogate-loss minimization) loss that progressively scales perturbation strength, maintaining 85.10% clean accuracy on CIFAR-10 (Canadian Institute for Advanced Research 10-class dataset) while improving robustness; a hybrid runtime defense integrating feature manipulation, statistical anomaly detection, and… More >

Yoshihiro Kanno^1,*, Izuru Takewaki²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 115-152, 2025, DOI:10.32604/cmes.2025.061551 - 11 April 2025

Abstract Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization. This paper provides a review on optimization-based methods for uncertainty analysis, with focusing attention on specific properties of adopted numerical optimization approaches. We collect and discuss the methods based on nonlinear programming, semidefinite programming, mixed-integer programming, mathematical programming with complementarity constraints, difference-of-convex programming, optimization methods using surrogate models and machine learning techniques, and metaheuristics. As a closely related topic, we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling. We conclude the paper by drawing several remarks through More >

Vidyalakshmi G¹, S Gopikrishnan^2,*, Wadii Boulila³, Anis Koubaa³, Gautam Srivastava^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 51-113, 2025, DOI:10.32604/cmes.2025.057788 - 11 April 2025

Abstract Cyber-Physical Systems (CPS) represent an integration of computational and physical elements, revolutionizing industries by enabling real-time monitoring, control, and optimization. A complementary technology, Digital Twin (DT), acts as a virtual replica of physical assets or processes, facilitating better decision making through simulations and predictive analytics. CPS and DT underpin the evolution of Industry 4.0 by bridging the physical and digital domains. This survey explores their synergy, highlighting how DT enriches CPS with dynamic modeling, real-time data integration, and advanced simulation capabilities. The layered architecture of DTs within CPS is examined, showcasing the enabling technologies and… More >

Zhengshun Fei^1,*, Jianxin Chen¹, Gui Chen², Xinjian Xiang^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4237-4261, 2025, DOI:10.32604/cmc.2025.059262 - 06 March 2025

Abstract Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics. However, metric learning methods often suffer from high sensitivity, leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior. Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples. However, it still faces the challenge of overfitting. Although curriculum learning-based adversarial training somewhat mitigates this issue, challenges still exist, such as the lack of a quantitative… More >

Balsam Dhyia Majeed^1,2, Amir Hossein Taherinia^1,*, Hadi Sadoghi Yazdi¹, Ahad Harati¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1027-1047, 2025, DOI:10.32604/cmc.2024.059789 - 03 January 2025

Abstract Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright. The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification. Some of these features are important perceptual features according to the human visual system (HVS), which means that the embedded watermark should be imperceptible in these features. Therefore, both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions. The two roles will be considered in this paper… More >