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Ijaz Haq
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100811, China

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Journal article
Published: 24 May 2021 in Sustainability
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Neural relation extraction (NRE) models are the backbone of various machine learning tasks, including knowledge base enrichment, information extraction, and document summarization. Despite the vast popularity of these models, their vulnerabilities remain unknown; this is of high concern given their growing use in security-sensitive applications such as question answering and machine translation in the aspects of sustainability. In this study, we demonstrate that NRE models are inherently vulnerable to adversarially crafted text that contains imperceptible modifications of the original but can mislead the target NRE model. Specifically, we propose a novel sustainable term frequency-inverse document frequency (TFIDF) based black-box adversarial attack to evaluate the robustness of state-of-the-art CNN, CGN, LSTM, and BERT-based models on two benchmark RE datasets. Compared with white-box adversarial attacks, black-box attacks impose further constraints on the query budget; thus, efficient black-box attacks remain an open problem. By applying TFIDF to the correctly classified sentences of each class label in the test set, the proposed query-efficient method achieves a reduction of up to 70% in the number of queries to the target model for identifying important text items. Based on these items, we design both character- and word-level perturbations to generate adversarial examples. The proposed attack successfully reduces the accuracy of six representative models from an average F1 score of 80% to below 20%. The generated adversarial examples were evaluated by humans and are considered semantically similar. Moreover, we discuss defense strategies that mitigate such attacks, and the potential countermeasures that could be deployed in order to improve sustainability of the proposed scheme.

ACS Style

Ijaz Haq; Zahid Khan; Arshad Ahmad; Bashir Hayat; Asif Khan; Ye-Eun Lee; Ki-Il Kim. Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks. Sustainability 2021, 13, 5892 .

AMA Style

Ijaz Haq, Zahid Khan, Arshad Ahmad, Bashir Hayat, Asif Khan, Ye-Eun Lee, Ki-Il Kim. Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks. Sustainability. 2021; 13 (11):5892.

Chicago/Turabian Style

Ijaz Haq; Zahid Khan; Arshad Ahmad; Bashir Hayat; Asif Khan; Ye-Eun Lee; Ki-Il Kim. 2021. "Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks." Sustainability 13, no. 11: 5892.

Journal article
Published: 23 November 2020 in Engineering Applications of Artificial Intelligence
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Although matrix factorization (MF) based collaborative filtering (CF) and deep learning approaches have achieved great success, there is still much room for improvement in recommender systems. Most of the existing approaches mainly adopt product ratings, reviews or content features in order to predict unknown rating for a user–item pair. In the discourse matter, some recent works attempted to obtain better latent representations of users and items by integrating different multi-source data, however, the heterogeneity of data is still a problem deserving study. Such models usually face two issues: (1) They extract the representations in a static and independent manner, thus ignoring the correlations between latent features learned from different information sources. (2) There is no unified framework that can mutually learn latent features from different sources such as ratings, reviews and meta-data of users, items and reviews. In the proposed model, called A Deep Hybrid Model for Recommendation (DHMR), we propose a joint deep model for learning higher-order non-linear latent feature interactions from reviews and metadata information. Further, we incorporate user–item interactions (from user–item ratings matrix) adopting MF model into the neural network. Thus, the proposed model consists of two parallel neural networks and an MF based model that are integrated by the attention and MLP layers at the top, learning lower-order (linear and non-linear) feature interactions of users and items separately and higher-order non-linear feature interactions jointly. Extensive experiments on real-world datasets demonstrate that DHMR significantly outperforms state-of-the-art recommendation models.

ACS Style

Zahid Younas Khan; Zhendong Niu; Ally S. Nyamawe; Ijaz Ul Haq. A Deep Hybrid Model for Recommendation by jointly leveraging ratings, reviews and metadata information. Engineering Applications of Artificial Intelligence 2020, 97, 104066 .

AMA Style

Zahid Younas Khan, Zhendong Niu, Ally S. Nyamawe, Ijaz Ul Haq. A Deep Hybrid Model for Recommendation by jointly leveraging ratings, reviews and metadata information. Engineering Applications of Artificial Intelligence. 2020; 97 ():104066.

Chicago/Turabian Style

Zahid Younas Khan; Zhendong Niu; Ally S. Nyamawe; Ijaz Ul Haq. 2020. "A Deep Hybrid Model for Recommendation by jointly leveraging ratings, reviews and metadata information." Engineering Applications of Artificial Intelligence 97, no. : 104066.