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  • Open Access


    Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

    Chengsheng Yuan1,2, Baojie Cui1,2, Zhili Zhou3, Xinting Li4,*, Qingming Jonathan Wu5

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

    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 to the blank area of… More >

  • Open Access


    A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

    Kun Fang, Julong Pan*, Lingyi Li, Ruihan Xiang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 493-514, 2024, DOI:10.32604/cmc.2023.045008

    Abstract With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance problems. Furthermore, a quantile-based approach… More >

  • Open Access


    Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks: An Empirical Study

    Shahad Alzahrani1, Hatim Alsuwat2, Emad Alsuwat3,*

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

    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 utilizes latent variables to quantify… More >

  • Open Access


    Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart Grids

    Fengyong Li1,*, Weicheng Shen1, Zhongqin Bi1, Xiangjing Su2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2095-2115, 2024, DOI:10.32604/cmes.2023.044431

    Abstract False data injection attack (FDIA) is an attack that affects the stability of grid cyber-physical system (GCPS) by evading the detecting mechanism of bad data. Existing FDIA detection methods usually employ complex neural network models to detect FDIA attacks. However, they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse, making it difficult for neural network models to obtain sufficient samples to construct a robust detection model. To address this problem, this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge, which can effectively bypass the detection model to… More >

  • Open Access


    Is There a Specific Profile of COVID-19 Risk Perception among People with Cancer? A Cross-Sectional Study

    Existe-t-il un profil spécifique de perception du risque de COVID-19 chez les personnes atteintes d’un cancer ? une étude transversale

    Renaud Mabire-Yon1,*, Arnaud Siméone1, Thibaud Marmorat2, Anne-Sophie Petit1, Mathilde Perray1, Costanza Puppo1, Charlotte Bauquier1, Claire Della Vecchia1, Hervé Picard3, Marie Préau1

    Psycho-Oncologie, Vol.17, No.4, pp. 245-256, 2023, DOI:10.32604/po.2023.042296

    Abstract Aims: This study aimed to determine if people with cancer (PWC) exhibit a unique COVID-19 risk perception profile and identify psychosocial factors characterizing PWC who do not conform to the majority risk perception profile. Procedure: A cross-sectional online self-questionnaire study was conducted in France from April 25 to May 07, 2020, with a sample (n = 748) comprising PWC, individuals not currently receiving cancer treatment, and those without a history of cancer. Latent profiles of COVID-19 risk perception (PCRP) were established. Methods: A multivariate multinomial logistic regression was performed to assess the association between cancer status and PCRP membership. Characteristics… More >

  • Open Access


    Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

    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

    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 records of patients. Once an… More >

  • Open Access


    Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

    Yue Mei1,2,3, Jianwei Deng1,2, Dongmei Zhao1,2, Changjiang Xiao1,2, Tianhang Wang4, Li Dong5, Xuefeng Zhu1,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 911-935, 2024, DOI:10.32604/cmes.2023.043810

    Abstract Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues. The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing. To address this issue, we propose a deep learning (DL) model based on conditional Generative Adversarial Networks (cGANs) to improve the quality of nonhomogeneous shear modulus reconstruction. To train this model, we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution. Both the simulated and experimental displacement fields are used to validate the proposed method. The reconstructed… More >

  • Open Access


    Use of Geographic Information System and Digital Elevation Model to Analyze the Hydro-Morphometric Characteristics of the Tshopo River Sub-Catchments, Democratic Republic of Congo

    Utilisation du système d’information géographique et modèle numérique de terrain dans l’analyse des caractéristiques hydro-morphométriques des sous-bassins versants de la rivière Tshopo, République démocratique du Congo

    Faidance Mashauri1,2,*, Mokili Mbuluyo1,3, Nsalambi Nkongolo2,4

    Revue Internationale de Géomatique, Vol.32, pp. 99-122, 2023, DOI:10.32604/rig.2023.044899

    Abstract The analysis and quantification of hydro-morphometric characteristics are essential for better management of water resources and more effective planning of hydroelectric projects in the Tshopo basin. Unfortunately, few studies have been carried out to assess these characteristics at the scale of this basin. Our methodological approach consists of using Geographic Information System (GIS) software analysis tools applied to the Digital Elevation Model (DEM) derived from the Advanced Land Observing Satellite (ALOS) World 3D-30m image. This enabled us to automatically extract the hydrographic network and generate the Tshopo sub-watersheds. The results of this analysis show that the Tshopo catchment area is… More >

  • Open Access


    Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

    Derwin Suhartono1,*, Alif Tri Handoyo1, Franz Adeta Junior2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3637-3657, 2023, DOI:10.32604/cmc.2023.045301

    Abstract Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication. This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network (GAN) based augmentation on diverse datasets, including iSarcasm, SemEval-18, and Ghosh. This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network (RGAN). The proposed RGAN method works by inverting labels between original and synthetic data during the training process. This inversion of labels provides feedback to the generator for generating… More >

  • Open Access


    PP-GAN: Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN

    Jongwook Si1, Sungyoung Kim2,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3119-3138, 2023, DOI:10.32604/cmc.2023.043797

    Abstract The objective of style transfer is to maintain the content of an image while transferring the style of another image. However, conventional methods face challenges in preserving facial features, especially in Korean portraits where elements like the “Gat” (a traditional Korean hat) are prevalent. This paper proposes a deep learning network designed to perform style transfer that includes the “Gat” while preserving the identity of the face. Unlike traditional style transfer techniques, the proposed method aims to preserve the texture, attire, and the “Gat” in the style image by employing image sharpening and face landmark, with the GAN. The color,… More >

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