Towards a Robust Deep Neural Network in Texts: A Survey

Wang, Wenqi; Wang, Run; Wang, Lina; Wang, Zhen; Ye, Aoshuang

doi:10.48550/arxiv.1902.07285

Cited by 12 publications

(22 citation statements)

References 115 publications

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“…In this method, each element in the input text is considered for substitution and the best perturbations are selected from all possible perturbations and rerun until no more perturbations are possible [15]. This attack method has been utilized in several research works [15], [43], [90] with various promising results. For example, Barham et al [43] introduced a sparse projected gradient descent (SPGD) method for crafting interpretable AEs for text applications.…”

Section: Breaching Security By Improving Attacksmentioning

confidence: 99%

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Omar¹,

Choi²,

Nyang³

et al. 2022

Preprint

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Recent natural language processing (NLP) techniques have accomplished high performance on benchmark datasets, primarily due to the significant improvement in the performance of deep learning. The advances in the research community have led to great enhancements in state-of-the-art production systems for NLP tasks, such as virtual assistants, speech recognition, and sentiment analysis. However, such NLP systems still often fail when tested with adversarial attacks. The initial lack of robustness exposed troubling gaps in current models' language understanding capabilities, creating problems when NLP systems are deployed in real life. In this paper, we present a structured overview of NLP robustness research by summarizing the literature in a systemic way across various dimensions. We then take a deep-dive into the various dimensions of robustness, across techniques, metrics, embeddings, and benchmarks. Finally, we argue that robustness should be multi-dimensional, provide insights into current research, identify gaps in the literature to suggest directions worth pursuing to address these gaps.

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Section: Breaching Security By Improving Attacksmentioning

confidence: 99%

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Omar¹,

Choi²,

Nyang³

et al. 2022

Preprint

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Section: Related Surveysmentioning

confidence: 99%

“…The literature on adversarial text analysis is quite rich [49,50,51]. Want et al [49] provide an overview of the literature on adversarial attacks and corresponding defense strategies for DNN-based English and Chinese text analysis systems. Zhang et al [50], provide a more detailed survey of adversarial attacks on deep learning-based models for NLP with a particular focus on adversarial textual examples generation methods.…”

Section: Related Surveysmentioning

confidence: 99%

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Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

Alsmadi¹,

Ahmad²,

Nazzal³

et al. 2021

Preprint

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The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing (NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these ML and NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this paper, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications, namely (i) rumors detection, (ii) satires detection, (iii) clickbaits & spams identification, (iv) hate speech detection, (v) misinformation detection, and (vi) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.

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“…To thwart adversarial attacks, various defense methods have been proposed to protect DNN models. In general, defense methods can be classified into two categories: detection and model enhancement [41]. For the former, defenders try to detect adversarial examples so can shield the model from them.…”

Section: Defense and Robustnessmentioning

confidence: 99%

Adversarial Robustness of Deep Code Comment Generation

Zhou¹,

Zhang²,

Shen³

et al. 2021

Preprint

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Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, or natural language processing. Recently they also have been applied to various software engineering tasks, typically involving processing source code. DNNs are well-known to be vulnerable to adversarial examples, i.e., fabricated inputs that could lead to various misbehaviors of the DNN model while being perceived as benign by humans. In this paper, we focus on the code comment generation task in software engineering and study the robustness issue of the DNNs when they are applied to this task. We propose ACCENT, an identifier substitution approach to craft adversarial code snippets, which are syntactically correct and functionality-preserving with respect to the original code snippet, but may mislead the DNNs to produce completely irrelevant code comments. In order to improve the robustness, ACCENT also incorporates a novel training method, which can be applied to existing code comment generation models. We conduct comprehensive experiments to evaluate our approach by attacking the mainstream encoder-decoder architectures on two large-scale publicly available datasets. The results show that ACCENT efficiently produces stable attacks with functionality-preserving adversarial examples, and the generated examples have better transferability compared with baselines. We also confirm, via experiments, the effectiveness in improving model robustness with our training method.

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Towards a Robust Deep Neural Network in Texts: A Survey

Cited by 12 publications

References 115 publications

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

Adversarial Robustness of Deep Code Comment Generation

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