Q-Detection: A Quantum-Classical Hybrid Poisoning Attack Detection Method
Authors: Haoqi He, Xiaokai Lin, Jiancai Chen, Yan Xiao
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results using multiple quantum simulation libraries show that Q-Detection effectively defends against label manipulation and backdoor attacks. The metrics demonstrate that Q-Detection consistently outperforms the baseline methods and is comparable to the state-of-the-art. Theoretical analysis shows that Q-Detection is expected to achieve more than a 20% speedup using quantum computing power. |
| Researcher Affiliation | Academia | School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology, including the bilevel optimization process, QUBO model, and training rules, in textual form. It does not contain a formally structured pseudocode or algorithm block. |
| Open Source Code | Yes | The demonstration experiment code can be found at https://github.com/cats1520cakes/Q-Detection-A-Quantum Classical-Hybrid-Poisoning-Attack-Detection-Method. |
| Open Datasets | Yes | The experiments are based on the GTSRB traffic sign image dataset, employing various common poisoning attack methods such as Targeted Label Flipping attacks, Bad Nets attacks, and Narcissus backdoor attacks [Jha et al., 2023; Zeng et al., 2023b; Gu et al., 2019] to evaluate the performance of Q-Detection. |
| Dataset Splits | Yes | GTSRB is widely used for traffic sign recognition, containing images of 43 categories of traffic signs, with 39,209 training images and 12,630 test images. We used Res Net-18 as the base model for training on the GTSRB dataset. |
| Hardware Specification | Yes | All experiments were conducted on a computer with an Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz with 64 GB RAM and an NVIDIA Ge Force RTX 3090 24GB GPU. |
| Software Dependencies | No | The paper mentions software like Kaiwu SDK, D-Wave, Qiskit Python API, PyTorch framework, and CUDA, but does not provide specific version numbers for these components. |
| Experiment Setup | Yes | We set the batch size to 179, requiring each Q-WAN to train for 220 epochs. Notably, using simulated QA can achieve an average training speed of 1 minute and 10 seconds, which is already very close to the CUDA-accelerated Meta-Sift training time. This suggests promising direct acceleration effects on real Quantum Annealer hardware. When we set the batch size to approximately one-quarter of the original size, i.e., 44, each Weight-Assigning Network needs to train for 892 epochs, resulting in an overall training time of 69 minutes. |