Jiakang Sun^1,2, Ke Chen^1,2, Xinyang He^1,2, Xu Liu^1,2, Ke Li^1,2, Cheng Peng^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 219-238, 2025, DOI:10.32604/cmc.2025.059745 - 26 March 2025

Abstract With the advancements in parameter-efficient transfer learning techniques, it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions. However, applying this technique to multimodal knowledge transfer introduces a significant challenge: ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task adaptation. This paper introduces UniTrans, a framework aimed at facilitating efficient knowledge transfer across multiple modalities. UniTrans leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter overhead. To further enhance modality alignment, we introduce two key components: the Multimodal More >

Zeric Tabekoueng Njitacke¹, Louai A. Maghrabi², Musheer Ahmad^3,*, Turki Althaqafi⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 917-933, 2025, DOI:10.32604/cmc.2025.059640 - 26 March 2025

Abstract This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map. The map demonstrates remarkable chaotic dynamics over a wide range of parameters. We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map, which allows us to select optimal parameter configurations for the encryption process. Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors, an essential characteristic for effective encryption. The encryption technique is based on bit-plane decomposition, wherein a plain image is divided into distinct… More >

Junpeng Wu^1,2,*, Xingfan Jiang²

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 897-915, 2025, DOI:10.32604/cmc.2025.058714 - 26 March 2025

Abstract As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance, the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical equipment. In response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis, we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once (GD-YOLO). Firstly, a partial convolution group is designed based on different convolution kernels. We combine the partial convolution group with… More >

Muath AlShaikh^1,*, Ahmed Manea Alkhalifah², Sultan Alamri³

Computer Systems Science and Engineering, Vol.49, pp. 353-376, 2025, DOI:10.32604/csse.2025.062433 - 21 March 2025

Abstract Data security is crucial for improving the confidentiality, integrity, and authenticity of the image content. Maintaining these security factors poses significant challenges, particularly in healthcare, business, and social media sectors, where information security and personal privacy are paramount. The cryptography concept introduces a solution to these challenges. This paper proposes an innovative hybrid image encryption algorithm capable of encrypting several types of images. The technique merges the Tiny Encryption Algorithm (TEA) and Rivest-Shamir-Adleman (RSA) algorithms called (TEA-RSA). The performance of this algorithm is promising in terms of cost and complexity, an encryption time which is… More >

Huiyao Dong¹, Igor Kotenko^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4263-4285, 2025, DOI:10.32604/cmc.2025.062439 - 06 March 2025

Abstract The growing complexity of cyber threats requires innovative machine learning techniques, and image-based malware classification opens up new possibilities. Meanwhile, existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection, and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges. This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis. The… More >

Sachin Sharma^1,2,*, Brajesh Kumar Singh³, Hitendra Garg²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4691-4708, 2025, DOI:10.32604/cmc.2025.061252 - 06 March 2025

Abstract Image tampering detection and localization have emerged as a critical domain in combating the pervasive issue of image manipulation due to the advancement of the large-scale availability of sophisticated image editing tools. The manual forgery localization is often reliant on forensic expertise. In recent times, machine learning (ML) and deep learning (DL) have shown promising results in automating image forgery localization. However, the ML-based method relies on hand-crafted features. Conversely, the DL method automatically extracts shallow spatial features to enhance the accuracy. However, DL-based methods lack the global co-relation of the features due to this… More >

Pengcheng Gao^*, Zhenjiang Li

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4555-4572, 2025, DOI:10.32604/cmc.2025.060873 - 06 March 2025

Abstract The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles (UAV) has emerged as a prominent research focus. Due to the considerable distance between UAVs and the photographed objects, coupled with complex shooting environments, existing models often struggle to achieve accurate real-time target detection. In this paper, a You Only Look Once v8 (YOLOv8) model is modified from four aspects: the detection head, the up-sampling module, the feature extraction module, and the parameter optimization of positive sample screening, and the YOLO-S3DT model is proposed to improve the performance of More >

Linh Nguyen Thi My^1,2,*, Viet-Tuan Le³, Tham Vo¹, Vinh Truong Hoang^3,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3693-3740, 2025, DOI:10.32604/cmc.2025.060793 - 06 March 2025

Abstract Pill image recognition is an important field in computer vision. It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety. This survey examines the current state of pill image recognition, focusing on advancements, methodologies, and the challenges that remain unresolved. It provides a comprehensive overview of traditional image processing-based, machine learning-based, deep learning-based, and hybrid-based methods, and aims to explore the ongoing difficulties in the field. We summarize and classify the methods used in each article, compare the strengths and More >

Alaa Thobhani^1,*, Beiji Zou¹, Xiaoyan Kui^1,*, Amr Abdussalam², Muhammad Asim³, Mohammed ELAffendi³, Sajid Shah³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3943-3964, 2025, DOI:10.32604/cmc.2025.060788 - 06 March 2025

Abstract Image captioning, the task of generating descriptive sentences for images, has advanced significantly with the integration of semantic information. However, traditional models still rely on static visual features that do not evolve with the changing linguistic context, which can hinder the ability to form meaningful connections between the image and the generated captions. This limitation often leads to captions that are less accurate or descriptive. In this paper, we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic context. Our model strengthens the… More >

Kaiqiao Wang^1,2, Peng Liu^1,2,*, Chun Zhang^1,2

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4931-4948, 2025, DOI:10.32604/cmc.2025.060547 - 06 March 2025

Abstract Underwater target detection in forward-looking sonar (FLS) images is a challenging but promising endeavor. The existing neural-based methods yield notable progress but there remains room for improvement due to overlooking the unique characteristics of underwater environments. Considering the problems of low imaging resolution, complex background environment, and large changes in target imaging of underwater sonar images, this paper specifically designs a sonar images target detection Network based on Progressive sensitivity capture, named ProNet. It progressively captures the sensitive regions in the current image where potential effective targets may exist. Guided by this basic idea, the… More >