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  • Open Access

    ARTICLE

    Adversarial Attacks on Content-Based Filtering Journal Recommender Systems

    Zhaoquan Gu1, Yinyin Cai1, Sheng Wang1, Mohan Li1, *, Jing Qiu1, Shen Su1, Xiaojiang Du1, Zhihong Tian1

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1755-1770, 2020, DOI:10.32604/cmc.2020.010739 - 30 June 2020

    Abstract Recommender systems are very useful for people to explore what they really need. Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers. In order to improve the efficiency of selecting the most suitable journals for publishing their works, journal recommender systems (JRS) can automatically provide a small number of candidate journals based on key information such as the title and the abstract. However, users or journal owners may attack the system for their own purposes. In this paper, we discuss about the adversarial attacks More >

  • Open Access

    ARTICLE

    A Fast Two-Stage Black-Box Deep Learning Network Attacking Method Based on Cross-Correlation

    Deyin Li1, 2, Mingzhi Cheng3, Yu Yang1, 2, *, Min Lei1, 2, Linfeng Shen4

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 623-635, 2020, DOI:10.32604/cmc.2020.09800 - 20 May 2020

    Abstract Deep learning networks are widely used in various systems that require classification. However, deep learning networks are vulnerable to adversarial attacks. The study on adversarial attacks plays an important role in defense. Black-box attacks require less knowledge about target models than white-box attacks do, which means black-box attacks are easier to launch and more valuable. However, the state-of-arts black-box attacks still suffer in low success rates and large visual distances between generative adversarial images and original images. This paper proposes a kind of fast black-box attack based on the cross-correlation (FBACC) method. The attack is… More >

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