Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons

Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. Therefore, it is necessary to conduct a systematical testing for DNNs before they are deployed to real-world applications. Existing testing methods have provided fine-grained metrics based on neuron coverage and proposed various approaches to improve such metrics. However, it has been gradually realized that a higher neuron coverage does \textit{not} necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To address this challenge, we introduce the concept of excitable neurons based on Shapley value and design a novel white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor.Comment: 32 page

Adversarial Examples in the Physical World: A Survey

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding. In this paper, we address this gap by thoroughly examining the characteristics of PAEs within a practical workflow encompassing training, manufacturing, and re-sampling processes. By analyzing the links between physical adversarial attacks, we identify manufacturing and re-sampling as the primary sources of distinct attributes and particularities in PAEs. Leveraging this knowledge, we develop a comprehensive analysis and classification framework for PAEs based on their specific characteristics, covering over 100 studies on physical-world adversarial examples. Furthermore, we investigate defense strategies against PAEs and identify open challenges and opportunities for future research. We aim to provide a fresh, thorough, and systematic understanding of PAEs, thereby promoting the development of robust adversarial learning and its application in open-world scenarios.Comment: Adversarial examples, physical-world scenarios, attacks and defense

2023 Low-Power Computer Vision Challenge (LPCVC) Summary

This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC). Since 2015, LPCVC has been an international competition devoted to tackling the challenge of computer vision (CV) on edge devices. Most CV researchers focus on improving accuracy, at the expense of ever-growing sizes of machine models. LPCVC balances accuracy with resource requirements. Winners must achieve high accuracy with short execution time when their CV solutions run on an embedded device, such as Raspberry PI or Nvidia Jetson Nano. The vision problem for 2023 LPCVC is segmentation of images acquired by Unmanned Aerial Vehicles (UAVs, also called drones) after disasters. The 2023 LPCVC attracted 60 international teams that submitted 676 solutions during the submission window of one month. This article explains the setup of the competition and highlights the winners' methods that improve accuracy and shorten execution time.Comment: LPCVC 2023, website: https://lpcv.ai

Decreasing the use of edible oils in China using WeChat and theories of behavior change: study protocol for a randomized controlled trial.

The consumption of edible oils in China has increased rapidly in recent years, and the total amount of edible-oil intake in the country has ranked first in the world. The choice and intake of edible oils, as a source of fats, are important factors that affect people's health. Many chronic diseases are closely associated with high-calorie and saturated-fat intake. The influence of traditional concepts that promote the use of edible oils among women, particularly housewives, plays a key role in a household's diet and nutrition because the diet-related knowledge, attitude and behaviour of housewives are dominant factors in planning and preparing their family's meals. WeChat, which was developed by Tencent, is a multipurpose messaging, social media and mobile payment application (app) in China. Described by Forbes as one of the world's most powerful apps, WeChat provides considerable convenience in disseminating knowledge. Accordingly, this study aims to design a pilot intervention to decrease the use of edible oils in Chinese households. The intervention, which is based on theories of behaviour change, will be implemented through WeChat. The study design is a randomised controlled trial that adopts knowledge, attitude and practice, social cognitive and stages of change theories as theoretical models. A total of 800 housewives between the ages of 25 and 45 years will be recruited on WeChat and from the communities in four areas (including rural and urban) in Chongqing, China. A self-administered questionnaire will be used to collect information regarding age, educational level, occupation, family members, edible-oil intake habits, knowledge of edible oils and WeChat usage habits. A total of 200 participants will be selected and randomly assigned to two equal-sized groups: group A (the intervention group) and group B (the control group). Group A will receive health education regarding edible oils for four consecutive weeks, whereas group B will be treated as the blank control. Each participant will complete a battery of knowledge, attitude and behaviour tests immediately, 3 months and 6 months after the intervention. In addition, weight, moisture rate, fat rate, visceral fat level and body mass index will be calculated using a multifunctional weighing scale, namely, Tanita BC-601 (Japan). The study is currently in the design stage. This study aims to increase knowledge and awareness of the appropriate use of edible oils, thereby encouraging participants to change behaviour by decreasing the intake of unhealthy levels of edible oils. It will be the first intervention to investigate the use of edible oils in China through WeChat. We predict that receiving health education regarding edible oils through WeChat will substantially improve the knowledge and attitude of the respondents. The members of the intervention group will have increased awareness and will be willing to decrease their use of edible oils to remain healthy. Results of this study may provide scientific evidence for the effect of health education through WeChat on edible oil-intake behaviour, thereby offering a comprehensive intervention to decrease the use of edible oils and promote a healthy lifestyle. Chinese Clinical Trial Registry (primary registry in the World Health Organisation registry network): ChiCTR-IOR-17013472 . Registered on 21 November 2017

Analysis of influencing factors and anti-pinch measures for door pinch accidents in conventional public transportation

A safe and convenient bus service is important for improving the bus mode share. However, during the morning and evening peak travel periods, hurrying passengers may get caught in closing doors when they squeeze into a bus. We conducted a survey of bus door pinch accidents that occurred in Harbin, China for the past five years to investigate the factors contributing to such accidents. A total of 1465 samples were collected to explore the effects of passenger age, gender, and other factors on pinching accidents. The results of the logit regression analysis revealed that all six influencing factors significantly affect the occurrence of door pinch accidents. Juveniles, females, and slow-moving people are more susceptible to pinch accidents. Additionally, situations such as door congestion, rushing to get on or off the bus, and inappropriately located cameras increase the likelihood of such accidents. To further investigate the effects of variables on different scenarios, the accidents were classified into four categories based on their occurrence process and object and analyzed using multiple logistic regression. The results show that juveniles and females are more likely to be involved in accidents in which objects are pinched, slow-moving people are more prone to be pinched while boarding and rushing to get on or off the bus, and congestion at the doors increases the probability of all four types of pinch accidents. Therefore, we recommend modifying the door position, eliminating the front door seat, and adjusting the handrail distribution to alleviate door congestion. Moreover, the door-closing warning sound must be amplified to remind passengers that the door is about to close, potentially reducing the number of pinching accidents caused by rushing to get on or off the bus