Xiaoyin Yi^1,2, Long Chen^1,3,4,*, Jiacheng Huang¹, Ning Yu¹, Qian Huang⁵

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 157-175, 2025, DOI:10.32604/cmc.2025.059863 - 26 March 2025

Abstract Transfer-based Adversarial Attacks (TAAs) can deceive a victim model even without prior knowledge. This is achieved by leveraging the property of adversarial examples. That is, when generated from a surrogate model, they retain their features if applied to other models due to their good transferability. However, adversarial examples often exhibit overfitting, as they are tailored to exploit the particular architecture and feature representation of source models. Consequently, when attempting black-box transfer attacks on different target models, their effectiveness is decreased. To solve this problem, this study proposes an approach based on a Regularized Constrained Feature More >

Donghyeok Park¹, Sumin Yeon², Hyeon Seo², Seok-Jun Buu², Suwon Lee^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2725-2737, 2025, DOI:10.32604/cmes.2025.061732 - 03 March 2025

Abstract Recent research on adversarial attacks has primarily focused on white-box attack techniques, with limited exploration of black-box attack methods. Furthermore, in many black-box research scenarios, it is assumed that the output label and probability distribution can be observed without imposing any constraints on the number of attack attempts. Unfortunately, this disregard for the real-world practicality of attacks, particularly their potential for human detectability, has left a gap in the research landscape. Considering these limitations, our study focuses on using a similar color attack method, assuming access only to the output label, limiting the number of More >

Khalil Ullah^1,*, Song Jian¹, Muhammad Naeem Ul Hassan¹, Suliman Khan², Mohammad Babar^3,*, Arshad Ahmad⁴, Shafiq Ahmad⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1151-1169, 2025, DOI:10.32604/cmc.2024.057328 - 03 January 2025

Abstract The emergence of next generation networks (NextG), including 5G and beyond, is reshaping the technological landscape of cellular and mobile networks. These networks are sufficiently scaled to interconnect billions of users and devices. Researchers in academia and industry are focusing on technological advancements to achieve high-speed transmission, cell planning, and latency reduction to facilitate emerging applications such as virtual reality, the metaverse, smart cities, smart health, and autonomous vehicles. NextG continuously improves its network functionality to support these applications. Multiple input multiple output (MIMO) technology offers spectral efficiency, dependability, and overall performance in conjunction with More >

Zhiyi Ding, Lei Sun^*, Xiuqing Mao, Leyu Dai, Ruiyang Ding

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4387-4412, 2024, DOI:10.32604/cmc.2024.052196 - 12 September 2024

Abstract Object detection finds wide application in various sectors, including autonomous driving, industry, and healthcare. Recent studies have highlighted the vulnerability of object detection models built using deep neural networks when confronted with carefully crafted adversarial examples. This not only reveals their shortcomings in defending against malicious attacks but also raises widespread concerns about the security of existing systems. Most existing adversarial attack strategies focus primarily on image classification problems, failing to fully exploit the unique characteristics of object detection models, thus resulting in widespread deficiencies in their transferability. Furthermore, previous research has predominantly concentrated on… 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 >

Chengsheng Yuan^1,2, Baojie Cui^1,2, Zhili Zhou³, Xinting Li^4,*, Qingming Jonathan Wu⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 899-914, 2024, DOI:10.32604/cmc.2023.045854 - 30 January 2024

Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added… More >

Shahad Alzahrani¹, Hatim Alsuwat², Emad Alsuwat^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1635-1654, 2024, DOI:10.32604/cmes.2023.044718 - 29 January 2024

Abstract Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables. However, the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams. One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks, wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance. In this research paper, we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms. Our framework… More >

Mustufa Haider Abidi^*, Hisham Alkhalefah, Mohamed K. Aboudaif

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 977-997, 2024, DOI:10.32604/cmes.2023.044169 - 30 December 2023

Abstract The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on the medical… More >

Jebran Khan¹, Kashif Ahmad², Kyung-Ah Sohn^1,3,*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159 - 09 November 2023

Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language processing (NLP) applications. However, these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication. These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form. The intuition of the proposed scheme… More >

Weizheng Wang^1,3, Xiangqi Wang^2,*, Xianmin Pan¹, Xingxing Gong³, Jian Liang³, Pradip Kumar Sharma⁴, Osama Alfarraj⁵, Wael Said⁶

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3859-3876, 2023, DOI:10.32604/cmc.2023.041346 - 08 October 2023

Abstract Image-denoising techniques are widely used to defend against Adversarial Examples (AEs). However, denoising alone cannot completely eliminate adversarial perturbations. The remaining perturbations tend to amplify as they propagate through deeper layers of the network, leading to misclassifications. Moreover, image denoising compromises the classification accuracy of original examples. To address these challenges in AE defense through image denoising, this paper proposes a novel AE detection technique. The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network (CNN) network structures. The used detector model integrates the classification results of different models as the input to… More >