Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs. This fixed-trigger design is a major weakness because many defenses detect or weaken the repeated patterns such triggers leave in data representations. Although input-aware dynamic backdoors have been studied in classical neural networks, transferring them to QNNs is difficult because quantum learning introduces new obstacles. In particular, measurement compresses the post-ansatz quantum state into a limited classical output, weakening supervision for a trigger generator, while individual density matrices fluctuate with the input and make per-sample contrastive learning unstable. To address these challenges, we propose Q-DIBA, the first input-aware dynamic backdoor attack for QNNs. Q-DIBA jointly trains a classical trigger generator and a victim QNN through a three-mode mini-batch strategy that supports clean behavior, attack activation, and trigger specificity. To provide stable quantum-level supervision, Q-DIBA introduces an ensemble density contrastive loss that operates on post-ansatz quantum states before measurement and contrasts mode-averaged density matrices rather than individual samples. Experiments on MNIST and Fashion-MNIST across multiple QNN architectures show that Q-DIBA achieves high clean accuracy, strong attack success, and high cross-trigger accuracy, demonstrating effectiveness, stealthiness, and input specificity. The attack also remains resilient against defenses including visual inspection, spectral-signature detection, and fine-tuning, suggesting that input-aware quantum backdoors are an important threat to secure QNN deployment.
Junrui Zhang, Zemin Chen, Lusi Li et al.· 0 citations
The rapid adoption of Internet of Things (IoT) devices has accelerated the need for privacy-preserving machine learning techniques, such as federated learning (FL). However, the decentralized and collaborative nature of FL makes it vulnerable to backdoor attacks, where adversaries locally update their malicious models before contributing to the global aggregation, subtly injecting backdoors without degrading the normal performance. An affected model behaves as expected during regular operations but exhibits malicious behavior when an embedded trigger is presented. In this article, we propose a novel self-supervised contrastive-learning-based approach to detect and mitigate backdoor attacks in FL within IoT environments. Unlike conventional reverse-engineering methods that iterate through each class in the dataset to reconstruct triggers, our approach directly regenerates triggers from compromised global models without class iteration. This is achieved by comparing last-layer feature representations of a potentially compromised model with those of a relatively clean model under the guidance of contrastive loss. The reverse-engineered trigger is then leveraged to patch the global model and remove the backdoors. We evaluate our method on three benchmark datasets under two federated backdoor attack scenarios, simulating IoT device collaborations. Extended experiments are also conducted on a transformer-based model and two mitigation methods to assess the robustness of our approach. Our results demonstrate that while traditional reverse-engineering techniques are effective in centralized settings, they struggle to detect backdoors in FL. Comparatively, our method is resilient against backdoor attacks across various settings. In addition, our method is more time-efficient because of its capability of generating the backdoor trigger directly without iterating through all classes.
Hal Ferguson, Rui Ning, Hongyi Wu et al.· IEEE Internet of Things Jour...· 0 citations