Model X-ray:Detect Backdoored Models via Decision Boundary
<|reference_start|>Model X-ray:Detect Backdoored Models via Decision Boundary: Deep neural networks (DNNs) have revolutionized various industries, leading to the rise of Machine Learning as a Service (MLaaS). In this paradigm, well-trained models are typically deployed through APIs. However, DNNs are susceptible to backdoor attacks, which pose significant risks to their applications. This vulnerability necessitates a method for users to ascertain whether an API is compromised before usage. Although many backdoor detection methods have been developed, they often operate under the assumption that the defender has access to specific information such as details of the attack, soft predictions from the model API, and even the knowledge of the model parameters, limiting their practicality in MLaaS scenarios. To address it, in this paper, we begin by presenting an intriguing observation: the decision boundary of the backdoored model exhibits a greater degree of closeness than that of the clean model. Simultaneously, if only one single label is infected, a larger portion of the regions will be dominated by the attacked label. Building upon this observation, we propose Model X-ray, a novel backdoor detection approach for MLaaS through the analysis of decision boundaries. Model X-ray can not only identify whether the target API is infected by backdoor attacks but also determine the target attacked label under the all-to-one attack strategy. Importantly, it accomplishes this solely by the hard prediction of clean inputs, regardless of any assumptions about attacks and prior knowledge of the training details of the model. Extensive experiments demonstrated that Model X-ray can be effective for MLaaS across diverse backdoor attacks, datasets, and architectures.<|reference_end|>
@article{su2024model,
    title={Model X-ray:Detecting Backdoored Models via Decision Boundary},
    author={Yanghao Su, Jie Zhang, Ting Xu, Tianwei Zhang, Weiming Zhang, Nenghai
  Yu},
    journal={arXiv preprint arXiv:2402.17465},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17465},
    primaryClass={cs.CR cs.CV}
}