Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an attacker-chosen target label while performing normally on other benign inputs. There are already numerous works on backdoor attacks on neural networks, but only a few works consider graph neural networks (GNNs). As such, there is no intensive research on explaining the impact of trigger injecting position on the performance of backdoor attacks on GNNs.
To bridge this gap, we conduct an experimental investigation on the performance of backdoor attacks on GNNs. We apply two powerful GNN explainability approaches to select the optimal trigger injecting position to achieve two attacker objectives - high attack success rate and low clean accuracy drop. Our empirical results on benchmark datasets and state-of-the-art neural network models demonstrate the proposed method’s effectiveness in selecting trigger injecting position for backdoor attacks on GNNs. For instance, on the node classification task, the backdoor attack with trigger injecting position selected by GraphLIME reaches over 84% attack success rate with less than 2.5% accuracy drop.
Behavioral biometric-based smartphone user authentication schemes based on touch/swipe have shown to provide the desired usability. However, their accuracy is not yet considered up to the mark. This is primarily due to the lack of a sufficient number of training samples, e.g., swiping gestures1: users are reluctant to provide many. Consequently, the application of such authentication techniques in the real world is still limited.
To overcome the shortage of training samples and make behavioral biometric-based schemes more accurate, we propose the usage of Generative Adversarial Networks (GAN) for generating synthetic samples, in our case, or swiping gestures. GAN is an unsupervised approach for synthetic data generation and has already been used in a wide range of applications, such as image and video generation. However, their use in behavioral biometric-based user authentication schemes has not been explored yet. In this paper, we propose SWIPEGAN - to generate swiping samples to be used for smartphone user authentication. Extensive experimentation and evaluation show the quality of the generated synthetic swiping samples and their efficacy in increasing the accuracy of the authentication scheme.