Research on detection and defense methods of backdoor attacks on quantum neural networks

Security research in quantum neural networks (QNNs) is an emerging field that aims to utilize the principles of quantum mechanics to protect the security of neural networks, and this protection can be against different threats such as eavesdropping, tampering, and malware attacks. Backdoor attacks are a serious security challenge in the field of deep neural networks. This type of attack is highly stealthy and harmful, which can silently implant malicious functions and trigger them under specific conditions, thus disrupting the normal operation of neural networks or leaking sensitive information. To address the potential security risks of backdoor attacks to QNNs, this paper focuses on two core issues: backdoor attack detection and defense strategy design. It proposes an activation-based clustering method for backdoor sample detection (ACDM) to counter backdoor attack threats. Experimental results demonstrate that this defense method effectively safeguards the operational security of QNNs while enhancing the model's robustness against abnormal inputs and the reliability of its output results. It should be noted that the current research has limitations: the adaptability and effectiveness of this defense framework on complex high-dimensional datasets (e.g., quantum image datasets) and QNNs with different topological structures (e.g., deep quantum convolutional networks) require further validation and optimization through subsequent experiments.