Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2022
Abstract
Deep Neural Networks (DNNs) have been widely adopted, yet DNN models are surprisingly unreliable, which raises significant concerns about their use in critical domains. In this work, we propose that runtime DNN mistakes can be quickly detected and properly dealt with in deployment, especially in settings like self-driving vehicles. Just as software engineering (SE) community has developed effective mechanisms and techniques to monitor and check programmed components, our previous work, SelfChecker, is designed to monitor and correct DNN predictions given unintended abnormal test data. SelfChecker triggers an alarm if the decisions given by the internal layer features of the model are inconsistent with the final prediction and provides advice in the form of an alternative prediction. In this paper, we extend SelfChecker to the security domain. Specifically, we describe SelfChecker++, which we designed to target both unintended abnormal test data and intended adversarial samples. Technically, we develop a technique which can transform any runtime inputs triggering alarms into semantically equivalent inputs, then we feed those transformed inputs to the model. Such runtime transformation nullifies any intended crafted samples, making the model immune to adversarial attacks that craft adversarial samples. We evaluated the alarm accuracy of SelfChecker++ on three DNN models and four popular image datasets, and found that SelfChecker++ triggers correct alarms on 63.10% of wrong DNN predictions, and triggers false alarms on 5.77% of correct DNN predictions. We also evaluated the effectiveness of SelfChecker++ in detecting adversarial examples and found it detects on average 70.09% of such samples with advice accuracy that is 20.89% higher than the original DNN model and 18.37% higher than SelfChecker.
Keywords
self-checking system, trustworthiness, deep neural networks, adversarial examples, deployment
Discipline
Artificial Intelligence and Robotics | OS and Networks
Research Areas
Intelligent Systems and Optimization
Publication
IEEE Transactions on Dependable and Secure Computing
First Page
1
Last Page
17
ISSN
1545-5971
Identifier
10.1109/TDSC.2022.3200421
Publisher
Institute of Electrical and Electronics Engineers
Citation
XIAO, Yan; BESCHASTNIKH, Ivan; LIN, Yun; HUNDAL, Rajdeep Singh; XIE, Xiaofei; ROSENBLUM, David S.; and DONG, Jin Song.
Self-checking deep neural networks for anomalies and adversaries in deployment. (2022). IEEE Transactions on Dependable and Secure Computing. 1-17.
Available at: https://ink.library.smu.edu.sg/sis_research/7493
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TDSC.2022.3200421