Sanxia Shi, Yinglei Song^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2911-2934, 2024, DOI:10.32604/cmc.2024.055363 - 18 November 2024

Abstract Infrared small target detection technology plays a pivotal role in critical military applications, including early warning systems and precision guidance for missiles and other defense mechanisms. Nevertheless, existing traditional methods face several significant challenges, including low background suppression ability, low detection rates, and high false alarm rates when identifying infrared small targets in complex environments. This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach. The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.… More >

Xiong Zhang^1,2, Minqing Zhang^1,2,3,*, Xu’an Wang^1,2,3,*, Siyuan Huang^1,2, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1433-1448, 2024, DOI:10.32604/cmc.2024.056742 - 15 October 2024

Abstract Traditional information hiding techniques achieve information hiding by modifying carrier data, which can easily leave detectable traces that may be detected by steganalysis tools. Especially in image transmission, both geometric and non-geometric attacks can cause subtle changes in the pixels of the image during transmission. To overcome these challenges, we propose a constructive robust image steganography technique based on style transformation. Unlike traditional steganography, our algorithm does not involve any direct modifications to the carrier data. In this study, we constructed a mapping dictionary by setting the correspondence between binary codes and image categories and… More >

Tongan Yu¹, Yali Xue^1,*, Yiming He¹, Shan Cui², Jun Hong²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1235-1250, 2024, DOI:10.32604/cmc.2024.056075 - 15 October 2024

Abstract With the rapid development of deep learning-based detection algorithms, deep learning is widely used in the field of infrared small target detection. However, well-designed adversarial samples can fool human visual perception, directly causing a serious decline in the detection quality of the recognition model. In this paper, an adversarial defense technology for small infrared targets is proposed to improve model robustness. The adversarial samples with strong migration can not only improve the generalization of defense technology, but also save the training cost. Therefore, this study adopts the concept of maximizing multidimensional feature distortion, applying noise… More >

Wenquan Sun^1,2, Jia Liu^1,2,*, Lifeng Chen^1,2, Weina Dong^1,2, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4031-4046, 2024, DOI:10.32604/cmc.2024.052745 - 12 September 2024

Abstract Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos (NeRV). While explicit methods exist for accurately embedding ownership or copyright information in video data, the nascent NeRV framework has yet to address this issue comprehensively. In response, this paper introduces MarkINeRV, a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV, which models the embedding and extraction of watermarks as a pair of… More >

Zengxiang Li¹, Yongchong Wu², Alanoud Al Mazroa³, Donghua Jiang⁴, Jianhua Wu⁵, Xishun Zhu^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 847-869, 2024, DOI:10.32604/cmes.2024.051762 - 20 August 2024

Abstract Hidden capacity, concealment, security, and robustness are essential indicators of hiding algorithms. Currently, hiding algorithms tend to focus on algorithmic capacity, concealment, and security but often overlook the robustness of the algorithms. In practical applications, the container can suffer from damage caused by noise, cropping, and other attacks during transmission, resulting in challenging or even impossible complete recovery of the secret image. An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms. In this proposed algorithm, a secret… More >

Zhibo Yang, Xiaohan Huang, Bingdong Wang, Bin Hu, Zhenyong Zhang^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3001-3019, 2024, DOI:10.32604/cmc.2024.053369 - 15 August 2024

Abstract With the widespread use of machine learning (ML) technology, the operational efficiency and responsiveness of power grids have been significantly enhanced, allowing smart grids to achieve high levels of automation and intelligence. However, tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks, making it urgent to enhance their robustness. To address this, we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles. Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws, ensuring training data accurately reflects possible More >

Lei Feng¹, Miao Liu¹, Yexun Yuan¹, Chi Zhang², Peng Geng^1,*

Journal on Internet of Things, Vol.6, pp. 1-16, 2024, DOI:10.32604/jiot.2024.053584 - 24 July 2024

Abstract With the rapid development of electric vehicles, the requirements for charging stations are getting higher and higher. In this study, we constructed a charging station topology network in Nanjing through the Space-L method, mapping charging stations as network nodes and constructing edges through road relationships. The experiment introduced five EV charging recommendation strategies (based on distance, number of fast charging piles, user preference, price, and overall rating) used to simulate disordered charging caused by different user preferences, and the impact of the network dynamic robustness in case of node failure is explored by simulating the… More >

Lifeng Chen^1,2, Jia Liu^1,2,*, Wenquan Sun^1,2, Weina Dong^1,2, Xiaozhong Pan^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1235-1250, 2024, DOI:10.32604/cmc.2024.051608 - 18 July 2024

Abstract This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field (NeRF) models. We employ an embedding network to integrate the watermark into the images within the training set. Then, the NeRF model is utilized for 3D modeling. For copyright verification, a secret image is generated by inputting a confidential viewpoint into NeRF. On this basis, design an extraction network to extract embedded watermark images from confidential viewpoints. In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario, the verifier can extract More >

Yue Wan, Nan Jiang^*, Ziyu Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3017-3034, 2024, DOI:10.32604/cmes.2024.051101 - 08 July 2024

Abstract The Social Internet of Things (SIoT) integrates the Internet of Things (IoT) and social networks, taking into account the social attributes of objects and diversifying the relationship between humans and objects, which overcomes the limitations of the IoT’s focus on associations between objects. Artificial Intelligence (AI) technology is rapidly evolving. It is critical to build trustworthy and transparent systems, especially with system security issues coming to the surface. This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT, aiming to build an SIoT hypergraph generation… More >

Xiong Zhang^1,2, Minqing Zhang^1,2,3,*, Xu An Wang^1,2,3, Wen Jiang^1,2, Chao Jiang^1,2, Pan Yang^1,4

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1925-1938, 2024, DOI:10.32604/cmc.2024.050899 - 15 May 2024

Abstract This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission. The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission. The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data. This process effectively enhances the concealment and imperceptibility of confidential information, thereby improving the security of such information during transmission and… More >