2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)最新文献

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Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing 从道路测试中评估自动驾驶汽车的安全性和可靠性

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2019-07-20 DOI: 10.1109/ISSRE.2019.00012

Xingyu Zhao, V. Robu, D. Flynn, K. Salako, L. Strigini

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引用次数: 42

TamperNN: Efficient Tampering Detection of Deployed Neural Nets TamperNN:部署神经网络的有效篡改检测

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2019-03-01 DOI: 10.1109/ISSRE.2019.00049

E. L. Merrer, Gilles Trédan

{"title":"TamperNN: Efficient Tampering Detection of Deployed Neural Nets","authors":"E. L. Merrer, Gilles Trédan","doi":"10.1109/ISSRE.2019.00049","DOIUrl":"https://doi.org/10.1109/ISSRE.2019.00049","url":null,"abstract":"Neural networks are powering the deployment of embedded devices and Internet of Things. Applications range from personal assistants to critical ones such as self-driving cars. It has been shown recently that models obtained from neural nets can be trojaned; an attacker can then trigger an arbitrary model behavior facing crafted inputs. This has a critical impact on the security and reliability of those deployed devices. We introduce novel algorithms to detect the tampering with deployed models, classifiers in particular. In the remote interaction setup we consider, the proposed strategy is to identify markers of the model input space that are likely to change class if the model is attacked, allowing a user to detect a possible tampering. This setup makes our proposal compatible with a wide range of scenarios, such as embedded models, or models exposed through prediction APIs. We experiment those tampering detection algorithms on the canonical MNIST dataset, over three different types of neural nets, and facing five different attacks (trojaning, quantization, fine-tuning, compression and watermarking). We then validate over five large models (VGG16, VGG19, ResNet, MobileNet, DenseNet) with a state of the art dataset (VGGFace2), and report results demonstrating the possibility of an efficient detection of model tampering.","PeriodicalId":254749,"journal":{"name":"2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130191025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

TripleAgent: Monitoring, Perturbation and Failure-Obliviousness for Automated Resilience Improvement in Java Applications TripleAgent: Java应用程序中自动弹性改进的监控、扰动和故障遗忘

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE) Pub Date : 2018-12-27 DOI: 10.1109/ISSRE.2019.00021

Long Zhang, Monperrus Martin

引用次数: 7

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