AGNES: Abstraction-guided Framework for Deep Neural Networks Security
This addresses security vulnerabilities in DNNs for safety-critical domains, but it is incremental as it builds on existing detection methods.
The paper tackles the problem of backdoors in deep neural networks for image recognition, particularly in safety-critical applications like autonomous driving, by introducing AGNES, a tool that detects these backdoors and performs better than many state-of-the-art methods in multiple case studies.
Deep Neural Networks (DNNs) are becoming widespread, particularly in safety-critical areas. One prominent application is image recognition in autonomous driving, where the correct classification of objects, such as traffic signs, is essential for safe driving. Unfortunately, DNNs are prone to backdoors, meaning that they concentrate on attributes of the image that should be irrelevant for their correct classification. Backdoors are integrated into a DNN during training, either with malicious intent (such as a manipulated training process, because of which a yellow sticker always leads to a traffic sign being recognised as a stop sign) or unintentional (such as a rural background leading to any traffic sign being recognised as animal crossing, because of biased training data). In this paper, we introduce AGNES, a tool to detect backdoors in DNNs for image recognition. We discuss the principle approach on which AGNES is based. Afterwards, we show that our tool performs better than many state-of-the-art methods for multiple relevant case studies.