GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks

While leveraging additional training data is well established to improve adversarial robustness, it incurs the unavoidable cost of data collection and the heavy computation to train models. To mitigate the costs, we propose \textit{Guided Adversarial Training } (GAT), a novel adversarial training technique that exploits auxiliary tasks under a limited set of training data. Our approach extends single-task models into multi-task models during the min-max optimization of adversarial training, and drives the loss optimization with a regularization of the gradient curvature across multiple tasks. GAT leverages two types of auxiliary tasks: self-supervised tasks, where the labels are generated automatically, and domain-knowledge tasks, where human experts provide additional labels. Experimentally, under limited data, GAT increases the robust accuracy on CIFAR-10 up to four times (from 11% to 42% robust accuracy) and the robust AUC of CheXpert medical imaging dataset from 50\% to 83\%. On the full CIFAR-10 dataset, GAT outperforms eight state-of-the-art adversarial training strategies. Our large study across five datasets and six tasks demonstrates that task augmentation is an efficient alternative to data augmentation, and can be key to achieving both clean and robust performances.

On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet and CIFAR, little research has considered real world applications, in particular in the medical domain. Our research shows that, contrary to previous claims, robustness of chest x-ray classification is much harder to evaluate and leads to very different assessments based on the dataset, the architecture and robustness metric. We argue that previous studies did not take into account the peculiarity of medical diagnosis, like the co-occurrence of diseases, the disagreement of labellers (domain experts), the threat model of the attacks and the risk implications for each successful attack. In this paper, we discuss the methodological foundations, review the pitfalls and best practices, and suggest new methodological considerations for evaluating the robustness of chest xray classification models. Our evaluation on 3 datasets, 7 models, and 18 diseases is the largest evaluation of robustness of chest x-ray classification models.

Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling

Graph neural networks (GNNs) have recently been popular in natural language and programming language processing, particularly in text and source code classification. Graph pooling which processes node representation into the entire graph representation, which can be used for multiple downstream tasks, e.g., graph classification, is a crucial component of GNNs. Recently, to enhance graph learning, Manifold Mixup, a data augmentation strategy that mixes the graph data vector after the pooling layer, has been introduced. However, since there are a series of graph pooling methods, how they affect the effectiveness of such a Mixup approach is unclear. In this paper, we take the first step to explore the influence of graph pooling methods on the effectiveness of the Mixup-based data augmentation approach. Specifically, 9 types of hybrid pooling methods are considered in the study, e.g., $\mathcal{M}_{sum}(\mathcal{P}_{att},\mathcal{P}_{max})$. The experimental results on both natural language datasets (Gossipcop, Politifact) and programming language datasets (Java250, Python800) demonstrate that hybrid pooling methods are more suitable for Mixup than the standard max pooling and the state-of-the-art graph multiset transformer (GMT) pooling, in terms of metric accuracy and robustness.

Enhancing Code Classification by Mixup-Based Data Augmentation

Recently, deep neural networks (DNNs) have been widely applied in programming language understanding. Generally, training a DNN model with competitive performance requires massive and high-quality labeled training data. However, collecting and labeling such data is time-consuming and labor-intensive. To tackle this issue, data augmentation has been a popular solution, which delicately increases the training data size, e.g., adversarial example generation. However, few works focus on employing it for programming language-related tasks. In this paper, we propose a Mixup-based data augmentation approach, MixCode, to enhance the source code classification task. First, we utilize multiple code refactoring methods to generate label-consistent code data. Second, the Mixup technique is employed to mix the original code and transformed code to form the new training data to train the model. We evaluate MixCode on two programming languages (JAVA and Python), two code tasks (problem classification and bug detection), four datasets (JAVA250, Python800, CodRep1, and Refactory), and 5 model architectures. Experimental results demonstrate that MixCode outperforms the standard data augmentation baseline by up to 6.24\% accuracy improvement and 26.06\% robustness improvement.

LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity

We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surrogate model and collects multiple weight sets from a few additional training epochs with a constant and high learning rate. LGV exploits two geometric properties that we relate to transferability. First, models that belong to a wider weight optimum are better surrogates. Second, we identify a subspace able to generate an effective surrogate ensemble among this wider optimum. Through extensive experiments, we show that LGV alone outperforms all (combinations of) four established test-time transformations by 1.8 to 59.9 percentage points. Our findings shed new light on the importance of the geometry of the weight space to explain the transferability of adversarial examples.

Efficient Testing of Deep Neural Networks via Decision Boundary Analysis

Deep learning plays a more and more important role in our daily life due to its competitive performance in multiple industrial application domains. As the core of DL-enabled systems, deep neural networks automatically learn knowledge from carefully collected and organized training data to gain the ability to predict the label of unseen data. Similar to the traditional software systems that need to be comprehensively tested, DNNs also need to be carefully evaluated to make sure the quality of the trained model meets the demand. In practice, the de facto standard to assess the quality of DNNs in industry is to check their performance (accuracy) on a collected set of labeled test data. However, preparing such labeled data is often not easy partly because of the huge labeling effort, i.e., data labeling is labor-intensive, especially with the massive new incoming unlabeled data every day. Recent studies show that test selection for DNN is a promising direction that tackles this issue by selecting minimal representative data to label and using these data to assess the model. However, it still requires human effort and cannot be automatic. In this paper, we propose a novel technique, named Aries, that can estimate the performance of DNNs on new unlabeled data using only the information obtained from the original test data. The key insight behind our technique is that the model should have similar prediction accuracy on the data which have similar distances to the decision boundary. We performed a large-scale evaluation of our technique on 13 types of data transformation methods. The results demonstrate the usefulness of our technique that the estimated accuracy by Aries is only 0.03% -- 2.60% (on average 0.61%) off the true accuracy. Besides, Aries also outperforms the state-of-the-art selection-labeling-based methods in most (96 out of 128) cases.

CodeS: A Distribution Shift Benchmark Dataset for Source Code Learning

Over the past few years, deep learning (DL) has been continuously expanding its applications and becoming a driving force for large-scale source code analysis in the big code era. Distribution shift, where the test set follows a different distribution from the training set, has been a longstanding challenge for the reliable deployment of DL models due to the unexpected accuracy degradation. Although recent progress on distribution shift benchmarking has been made in domains such as computer vision and natural language process. Limited progress has been made on distribution shift analysis and benchmarking for source code tasks, on which there comes a strong demand due to both its volume and its important role in supporting the foundations of almost all industrial sectors. To fill this gap, this paper initiates to propose CodeS, a distribution shift benchmark dataset, for source code learning. Specifically, CodeS supports 2 programming languages (i.e., Java and Python) and 5 types of code distribution shifts (i.e., task, programmer, time-stamp, token, and CST). To the best of our knowledge, we are the first to define the code representation-based distribution shifts. In the experiments, we first evaluate the effectiveness of existing out-of-distribution detectors and the reasonability of the distribution shift definitions and then measure the model generalization of popular code learning models (e.g., CodeBERT) on classification task. The results demonstrate that 1) only softmax score-based OOD detectors perform well on CodeS, 2) distribution shift causes the accuracy degradation in all code classification models, 3) representation-based distribution shifts have a higher impact on the model than others, and 4) pre-trained models are more resistant to distribution shifts. We make CodeS publicly available, enabling follow-up research on the quality assessment of code learning models.

Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment

Deep Neural Networks (DNNs) have gained considerable attention in the past decades due to their astounding performance in different applications, such as natural language modeling, self-driving assistance, and source code understanding. With rapid exploration, more and more complex DNN architectures have been proposed along with huge pre-trained model parameters. The common way to use such DNN models in user-friendly devices (e.g., mobile phones) is to perform model compression before deployment. However, recent research has demonstrated that model compression, e.g., model quantization, yields accuracy degradation as well as outputs disagreements when tested on unseen data. Since the unseen data always include distribution shifts and often appear in the wild, the quality and reliability of quantized models are not ensured. In this paper, we conduct a comprehensive study to characterize and help users understand the behaviors of quantized models. Our study considers 4 datasets spanning from image to text, 8 DNN architectures including feed-forward neural networks and recurrent neural networks, and 42 shifted sets with both synthetic and natural distribution shifts. The results reveal that 1) data with distribution shifts happen more disagreements than without. 2) Quantization-aware training can produce more stable models than standard, adversarial, and Mixup training. 3) Disagreements often have closer top-1 and top-2 output probabilities, and $Margin$ is a better indicator than the other uncertainty metrics to distinguish disagreements. 4) Retraining with disagreements has limited efficiency in removing disagreements. We opensource our code and models as a new benchmark for further studying the quantized models.

Labeling-Free Comparison Testing of Deep Learning Models

Various deep neural networks (DNNs) are developed and reported for their tremendous success in multiple domains. Given a specific task, developers can collect massive DNNs from public sources for efficient reusing and avoid redundant work from scratch. However, testing the performance (e.g., accuracy and robustness) of multiple DNNs and giving a reasonable recommendation that which model should be used is challenging regarding the scarcity of labeled data and demand of domain expertise. Existing testing approaches are mainly selection-based where after sampling, a few of the test data are labeled to discriminate DNNs. Therefore, due to the randomness of sampling, the performance ranking is not deterministic. In this paper, we propose a labeling-free comparison testing approach to overcome the limitations of labeling effort and sampling randomness. The main idea is to learn a Bayesian model to infer the models' specialty only based on predicted labels. To evaluate the effectiveness of our approach, we undertook exhaustive experiments on 9 benchmark datasets spanning in the domains of image, text, and source code, and 165 DNNs. In addition to accuracy, we consider the robustness against synthetic and natural distribution shifts. The experimental results demonstrate that the performance of existing approaches degrades under distribution shifts. Our approach outperforms the baseline methods by up to 0.74 and 0.53 on Spearman's correlation and Kendall's $\tau$, respectively, regardless of the dataset and distribution shift. Additionally, we investigated the impact of model quality (accuracy and robustness) and diversity (standard deviation of the quality) on the testing effectiveness and observe that there is a higher chance of a good result when the quality is over 50\% and the diversity is larger than 18\%.

On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks

While the literature on security attacks and defense of Machine Learning (ML) systems mostly focuses on unrealistic adversarial examples, recent research has raised concern about the under-explored field of realistic adversarial attacks and their implications on the robustness of real-world systems. Our paper paves the way for a better understanding of adversarial robustness against realistic attacks and makes two major contributions. First, we conduct a study on three real-world use cases (text classification, botnet detection, malware detection)) and five datasets in order to evaluate whether unrealistic adversarial examples can be used to protect models against realistic examples. Our results reveal discrepancies across the use cases, where unrealistic examples can either be as effective as the realistic ones or may offer only limited improvement. Second, to explain these results, we analyze the latent representation of the adversarial examples generated with realistic and unrealistic attacks. We shed light on the patterns that discriminate which unrealistic examples can be used for effective hardening. We release our code, datasets and models to support future research in exploring how to reduce the gap between unrealistic and realistic adversarial attacks.