Home / Journals / JAI / Vol.3, No.1, 2021
  • Open Access

    ARTICLE

    An Adversarial Attack System for Face Recognition

    Yuetian Wang, Chuanjing Zhang, Xuxin Liao, Xingang Wang, Zhaoquan Gu*
    Journal on Artificial Intelligence, Vol.3, No.1, pp. 1-8, 2021, DOI:10.32604/jai.2021.014175
    Abstract Deep neural networks (DNNs) are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers. Many related works show that DNNs are vulnerable to adversarial examples that are generated with subtle perturbation to original images in both digital domain and physical domain. As a most common application of DNNs, face recognition systems are likely to cause serious consequences if they are attacked by the adversarial examples. In this paper, we implement an adversarial attack system for face recognition in both digital domain that generates adversarial face images to fool… More >

  • Open Access

    ARTICLE

    An Anomaly Detection Method of Industrial Data Based on Stacking Integration

    Kunkun Wang1,2, Xianda Liu2,3,4,*
    Journal on Artificial Intelligence, Vol.3, No.1, pp. 9-19, 2021, DOI:10.32604/jai.2021.016706
    Abstract With the development of Internet technology, the computing power of data has increased, and the development of machine learning has become faster and faster. In the industrial production of industrial control systems, quality inspection and safety production of process products have always been our concern. Aiming at the low accuracy of anomaly detection in process data in industrial control system, this paper proposes an anomaly detection method based on stacking integration using the machine learning algorithm. Data are collected from the industrial site and processed by feature engineering. Principal component analysis (PCA) and integrated rule tree method are adopted to… More >

  • Open Access

    ARTICLE

    PS-Fuzz: Efficient Graybox Firmware Fuzzing Based on Protocol State

    Xiaoyi Li, Xiaojun Pan, Yanbin Sun*
    Journal on Artificial Intelligence, Vol.3, No.1, pp. 21-31, 2021, DOI:10.32604/jai.2021.017328
    Abstract The rise of the Internet of Things (IoT) exposes more and more important embedded devices to the network, which poses a serious threat to people’s lives and property. Therefore, ensuring the safety of embedded devices is a very important task. Fuzzing is currently the most effective technique for discovering vulnerabilities. In this work, we proposed PS-Fuzz (Protocol State Fuzz), a gray-box fuzzing technique based on protocol state orientation. By instrumenting the program that handles protocol fields in the firmware, the problem of lack of guidance information in common protocol fuzzing is solved. By recording and comparing state transition paths, the… More >

  • Open Access

    ARTICLE

    Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics Distribution for BBG Supermarket

    Yizhi Liu1,2, Rutian Qing1,2,*, Liangran Wu1,2, Min Liu1,2, Zhuhua Liao1,2, Yijiang Zhao1,2
    Journal on Artificial Intelligence, Vol.3, No.1, pp. 33-43, 2021, DOI:10.32604/jai.2021.016565
    Abstract In the large-scale logistics distribution of single logistic center, the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution. Addressing at this issue, we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG supermarket. We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm. Greedy algorithm is applied to initialize the population, and then hill-climbing algorithm is used to optimize individuals in each generation after selection, crossover and mutation. Our approach is evaluated on the dataset of BBG Supermarket which is one of the… More >

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