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  • Open Access

    ARTICLE

    Attention Guided Food Recognition via Multi-Stage Local Feature Fusion

    Gonghui Deng, Dunzhi Wu, Weizhen Chen*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1985-2003, 2024, DOI:10.32604/cmc.2024.052174

    Abstract The task of food image recognition, a nuanced subset of fine-grained image recognition, grapples with substantial intra-class variation and minimal inter-class differences. These challenges are compounded by the irregular and multi-scale nature of food images. Addressing these complexities, our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion, grounded in the ConvNeXt architecture. Our model employs hybrid attention (HA) mechanisms to pinpoint critical discriminative regions within images, substantially mitigating the influence of background noise. Furthermore, it introduces a multi-stage local fusion (MSLF) module, fostering long-distance dependencies between feature maps at… More >

  • Open Access

    ARTICLE

    FFRA: A Fine-Grained Function-Level Framework to Reduce the Attack Surface

    Xingxing Zhang1, Liang Liu1,*, Yu Fan1, Qian Zhou2

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 969-987, 2024, DOI:10.32604/csse.2024.046615

    Abstract System calls are essential interfaces that enable applications to access and utilize the operating system’s services and resources. Attackers frequently exploit application’s vulnerabilities and misuse system calls to execute malicious code, aiming to elevate privileges and so on. Consequently, restricting the misuse of system calls becomes a crucial measure in ensuring system security. It is an effective method known as reducing the attack surface. Existing attack surface reduction techniques construct a global whitelist of system calls for the entire lifetime of the application, which is coarse-grained. In this paper, we propose a Fine-grained Function-level framework… More >

  • Open Access

    ARTICLE

    Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset, Methodology and Evaluation

    Shiwen Song, Rui Zhang, Min Hu*, Feiyao Huang

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5243-5271, 2024, DOI:10.32604/cmc.2024.050879

    Abstract Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security. Currently, with the emergence of massive high-resolution multi-modality images, the use of multi-modality images for fine-grained recognition has become a promising technology. Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples. The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features. The attention mechanism helps the model to pinpoint the key information in the image, resulting in a… More >

  • Open Access

    ARTICLE

    A Blockchain and CP-ABE Based Access Control Scheme with Fine-Grained Revocation of Attributes in Cloud Health

    Ye Lu1,*, Tao Feng1, Chunyan Liu2, Wenbo Zhang3

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2787-2811, 2024, DOI:10.32604/cmc.2023.046106

    Abstract The Access control scheme is an effective method to protect user data privacy. The access control scheme based on blockchain and ciphertext policy attribute encryption (CP–ABE) can solve the problems of single—point of failure and lack of trust in the centralized system. However, it also brings new problems to the health information in the cloud storage environment, such as attribute leakage, low consensus efficiency, complex permission updates, and so on. This paper proposes an access control scheme with fine-grained attribute revocation, keyword search, and traceability of the attribute private key distribution process. Blockchain technology tracks… More >

  • Open Access

    ARTICLE

    Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

    Guoqing Zhou, Liang Huang, Qiao Sun*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1985-2007, 2023, DOI:10.32604/cmc.2023.040902

    Abstract The remote sensing ships’ fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For… More >

  • Open Access

    ARTICLE

    Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

    Wenkai Qin1, Tianliang Lu1,*, Lu Zhang2, Shufan Peng1, Da Wan1

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 467-490, 2023, DOI:10.32604/cmc.2023.042417

    Abstract With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance… More >

  • Open Access

    ARTICLE

    Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

    Ghoroub Talal Bostaji*, Emad Sami Jaha

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1571-1591, 2023, DOI:10.32604/csse.2023.039701

    Abstract Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues. Therefore, biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral characteristics. Among different viable biometric modalities, the human ear structure can offer unique and valuable discriminative characteristics for human recognition systems. In recent years, most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful results. Nevertheless, such traditional models can… More >

  • Open Access

    ARTICLE

    Fine-Grained Action Recognition Based on Temporal Pyramid Excitation Network

    Xuan Zhou1,*, Jianping Yi2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2103-2116, 2023, DOI:10.32604/iasc.2023.034855

    Abstract Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recognition. Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation. However, these methods failed to capture complex motion patterns due to their limited receptive field. To solve the above problems, this paper proposes a lightweight Temporal Pyramid Excitation (TPE) module to capture the short, medium, and long-term temporal context. In this method, Temporal Pyramid (TP) module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without More >

  • Open Access

    ARTICLE

    Fine-Grained Pornographic Image Recognition with Multi-Instance Learning

    Zhiqiang Wu*, Bing Xie

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 299-316, 2023, DOI:10.32604/csse.2023.038586

    Abstract Image has become an essential medium for expressing meaning and disseminating information. Many images are uploaded to the Internet, among which some are pornographic, causing adverse effects on public psychological health. To create a clean and positive Internet environment, network enforcement agencies need an automatic and efficient pornographic image recognition tool. Previous studies on pornographic images mainly rely on convolutional neural networks (CNN). Because of CNN’s many parameters, they must rely on a large labeled training dataset, which takes work to build. To reduce the effect of the database on the recognition performance of pornographic… More >

  • Open Access

    ARTICLE

    Fine-Grained Multivariate Time Series Anomaly Detection in IoT

    Shiming He1,4, Meng Guo1, Bo Yang1, Osama Alfarraj2, Amr Tolba2, Pradip Kumar Sharma3, Xi’ai Yan4,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5027-5047, 2023, DOI:10.32604/cmc.2023.038551

    Abstract Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that… More >

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