Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification. Despite its tremendous popularity, the security vulnerabilities of DLTU are still largely unknown, which is highly concerning given its increasing use in security-sensitive applications such as sentiment analysis and toxic content detection. In this paper, we show that DLTU is inherently vulnerable to adversarial text attacks, in which maliciously crafted texts trigger target DLTU systems and services to misbehave. Specifically, we present TextBugger, a general attack framework for generating adversarial texts. In contrast to prior works, TextBugger differs in significant ways: (i) effective -- it outperforms state-of-the-art attacks in terms of attack success rate; (ii) evasive -- it preserves the utility of benign text, with 94.9\% of the adversarial text correctly recognized by human readers; and (iii) efficient -- it generates adversarial text with computational complexity sub-linear to the text length. We empirically evaluate TextBugger on a set of real-world DLTU systems and services used for sentiment analysis and toxic content detection, demonstrating its effectiveness, evasiveness, and efficiency. For instance, TextBugger achieves 100\% success rate on the IMDB dataset based on Amazon AWS Comprehend within 4.61 seconds and preserves 97\% semantic similarity. We further discuss possible defense mechanisms to mitigate such attack and the adversary's potential countermeasures, which leads to promising directions for further research.

TextBugger: Generating Adversarial Text Against Real-world Applications

As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness, which often makes it hard to identify the most promising ideas in training robust models. A key challenge in benchmarking robustness is that its evaluation is often error-prone, leading to overestimation of the true robustness of models. While adaptive attacks designed for a particular defense are a potential solution, they have to be highly customized for particular models, which makes it difficult to compare different methods. Our goal is to instead establish a standardized benchmark of adversarial robustness, which as accurately as possible reflects the robustness of the considered models within a reasonable computational budget. To evaluate the robustness of models for our benchmark, we consider AutoAttack, an ensemble of white- and black-box attacks which was recently shown in a large-scale study to improve almost all robustness evaluations compared to the original publications. We also impose some restrictions on the admitted models to rule out defenses that only make gradient-based attacks ineffective without improving actual robustness. Our leaderboard, hosted at this https URL, contains evaluations of 90+ models and aims at reflecting the current state of the art on a set of well-defined tasks in $\ell_\infty$- and $\ell_2$-threat models and on common corruptions, with possible extensions in the future. Additionally, we open-source the library this https URL that provides unified access to 60+ robust models to facilitate their downstream applications. Finally, based on the collected models, we analyze the impact of robustness on the performance on distribution shifts, calibration, out-of-distribution detection, fairness, privacy leakage, smoothness, and transferability.

RobustBench: a standardized adversarial robustness benchmark.

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical ML and scalable general-purpose GPU computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence.

Deep learning (DL) models are inherently vulnerable to adversarial examples – maliciously crafted inputs to trigger target DL models to misbehave – which significantly hinders the application of DL in security-sensitive domains. Intensive research on adversarial learning has led to an arms race between adversaries and defenders. Such plethora of emerging attacks and defenses raise many questions: Which attacks are more evasive, preprocessing-proof, or transferable? Which defenses are more effective, utility-preserving, or general? Are ensembles of multiple defenses more robust than individuals? Yet, due to the lack of platforms for comprehensive evaluation on adversarial attacks and defenses, these critical questions remain largely unsolved. In this paper, we present the design, implementation, and evaluation of DEEPSEC, a uniform platform that aims to bridge this gap. In its current implementation, DEEPSEC incorporates 16 state-of-the-art attacks with 10 attack utility metrics, and 13 state-of-the-art defenses with 5 defensive utility metrics. To our best knowledge, DEEPSEC is the first platform that enables researchers and practitioners to (i) measure the vulnerability of DL models, (ii) evaluate the effectiveness of various attacks/defenses, and (iii) conduct comparative studies on attacks/defenses in a comprehensive and informative manner. Leveraging DEEPSEC, we systematically evaluate the existing adversarial attack and defense methods, and draw a set of key findings, which demonstrate DEEPSEC’s rich functionality, such as (1) the trade-off between misclassification and imperceptibility is empirically confirmed; (2) most defenses that claim to be universally applicable can only defend against limited types of attacks under restricted settings; (3) it is not necessary that adversarial examples with higher perturbation magnitude are easier to be detected; (4) the ensemble of multiple defenses cannot improve the overall defense capability, but can improve the lower bound of the defense effectiveness of individuals. Extensive analysis on DEEPSEC demonstrates its capabilities and advantages as a benchmark platform which can benefit future adversarial learning research.

/pdf/deepsec-a-uniform-platform-for-security-analysis-of-deep-23f1qf2qab.pdf

DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this paper, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.

Deep Graph Matching and Searching for Semantic Code Retrieval

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however, neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information. In this article, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval. To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets, but also learns the fine-grained similarity between them by cross-attention based semantic matching operations. We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python). Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.

While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to deep graph similarity learning. Recent work has considered either global-level graph-graph interactions or low-level node-node interactions, ignoring the rich cross-level interactions (e.g., between nodes and a whole graph). In this paper, we propose a Hierarchical Graph Matching Network (HGMN) for computing the graph similarity between any pair of graph-structured objects. Our model jointly learns graph representations and a graph matching metric function for computing graph similarities in an end-to-end fashion. The proposed HGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs. Our comprehensive experiments demonstrate that HGMN consistently outperforms state-of-the-art graph matching network baselines for both classification and regression tasks.

/pdf/hierarchical-graph-matching-networks-for-deep-graph-4s3kzezu27.pdf

Hierarchical Graph Matching Networks for Deep Graph Similarity Learning.

The problem of graph matching that tries to establish some kind of structural correspondence between a pair of graph-structured objects is one of the key challenges in a variety of real-world applications. In general, the graph matching problem can be classified into two categories: i) the classic graph matching problem which finds an optimal node-to-node correspondence between nodes of a pair of input graphs and ii) the graph similarity problem which computes a similarity metric between two graphs. While recent years have witnessed the great success of GNNs in learning node representations of graphs, there is an increasing interest in exploring GNNs for the graph matching problem in an end-to-end manner. This chapter focuses on the state of the art of graph matching models based on GNNs. We start by introducing some backgrounds of the graph matching problem. Then, for each category of graph matching problem, we provide a formal definition and discuss state-of-the-art GNN-based models for both the classic graph matching problem and the graph similarity problem, respectively. Finally, this chapter is concluded by pointing out some possible future research directions. 

Xiang Ling

Papers

DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model

Deep Graph Matching and Searching for Semantic Code Retrieval

Deep Graph Matching and Searching for Semantic Code Retrieval

Hierarchical Graph Matching Networks for Deep Graph Similarity Learning.

Graph Neural Networks: Graph Matching