Generating Adversarial Examples with Adversarial Networks

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.Comment: Accepted to IJCAI201

Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation

Deep Neural Networks (DNNs) have been widely applied in various recognition tasks. However, recently DNNs have been shown to be vulnerable against adversarial examples, which can mislead DNNs to make arbitrary incorrect predictions. While adversarial examples are well studied in classification tasks, other learning problems may have different properties. For instance, semantic segmentation requires additional components such as dilated convolutions and multiscale processing. In this paper, we aim to characterize adversarial examples based on spatial context information in semantic segmentation. We observe that spatial consistency information can be potentially leveraged to detect adversarial examples robustly even when a strong adaptive attacker has access to the model and detection strategies. We also show that adversarial examples based on attacks considered within the paper barely transfer among models, even though transferability is common in classification. Our observations shed new light on developing adversarial attacks and defenses to better understand the vulnerabilities of DNNs.Comment: Accepted to ECCV 201

Forecasting Future World Events with Neural Networks

Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). Motivated by the difficulty of forecasting numbers across orders of magnitude (e.g. global cases of COVID-19 in 2022), we also curate IntervalQA, a dataset of numerical questions and metrics for calibration. We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel challenge for large language models and improved performance could bring large practical benefits.Comment: NeurIPS 2022; our dataset is available at https://github.com/andyzoujm/autocas

The contribution of female community health volunteers (FCHVs) to maternity care in Nepal: a qualitative study.

BACKGROUND: In resource-poor settings, the provision of basic maternity care within health centres is often a challenge. Despite the difficulties, Nepal reduced its maternal mortality ratio by 80% from 850 to an estimated 170 per 100,000 live births between 1991 and 2011 to achieve Millennium Development Goal Five. One group that has been credited for this is community health workers, known as Female Community Health Volunteers (FCHVs), who form an integral part of the government healthcare system. This qualitative study explores the role of FCHVs in maternal healthcare provision in two regions: the Hill and Terai. METHODS: Between May 2014 and September 2014, 20 FCHVs, 11 health workers and 26 service users were purposefully selected and interviewed using semi-structured topic guides. In addition, four focus group discussions were held with 19 FCHVs. Data were analysed using thematic analysis. RESULTS: All study participants acknowledged the contribution of FCHVs in maternity care. All FCHVs reported that they shared key health messages through regularly held mothers' group meetings and referred women for health checks. The main difference between the two study regions was the support available to FCHVs from the local health centres. With regular training and access to medical supplies, FCHVs in the hill villages reported activities such as assisting with childbirth, distributing medicines and administering pregnancy tests. They also reported use of innovative approaches to educate mothers. Such activities were not reported in Terai. In both regions, a lack of monetary incentives was reported as a major challenge for already overburdened volunteers followed by a lack of education for FCHVs. CONCLUSIONS: Our findings suggest that the role of FCHVs varies according to the context in which they work. FCHVs, supported by government health centres with emphasis on the use of local approaches, have the potential to deliver basic maternity care and promote health-seeking behaviour so that serious delays in receiving healthcare can be minimised. However, FCHVs need to be reimbursed and provided with educational training to ensure that they can work effectively. The study underlines the relevance of community health workers in resource-poor settings