Abstract: Backdoor attacks cause deep neural network models to make misclassifications by embedding specific triggers during training.To address the common issues of poor trigger stealthiness,low poisoning efficiency,and easy detection of poisoned mo-dels in existing backdoor attack schemes,this paper proposes a stealthy backdoor attack method based on sensitivity optimization under low poisoning rates.By analyzing the dependency of deep neural networks on local semantic features and leveraging the fact that model misclassification is primarily dominated by feature activation in specific spatial regions,a sparse sensitivity mask optimization algorithm is designed to locate feature regions sensitive to the target class and generate trigger patterns that effectively influence these sensitive features.Meanwhile,a sensitive sample selection mechanism is adopted,which quantifies the poisoning value of candidate samples based on gradient norms and adaptively selects the subset with the greatest impact on backdoor triggering.Within this dual-optimization framework of “feature-sample” collaboration,the sparse sensitivity mask ensures precise implantation of triggers into feature regions critical to model decisions,while sensitive sample screening identifies the subset of samples that contribute most to backdoor activation from limited data.Through co-optimizing the spatial sensitivity of trigger patterns and the poisoning sensitivity of samples,this method achieves stealthy and highly effective backdoor attacks even under extremely low poisoning rates.Experimental results show that with a poisoning rate below 0.5%,the proposed method achieves an average attack success rate higher than 94% on the Cifar-10,Cifar-100,VGGFace2,and RImageNet datasets,outperforming exis-ting backdoor attack methods across multiple evaluation metrics and effectively evading detection by mainstream defense mechanisms such as Neural Cleanse,thus validating its effectiveness.

Key words: Backdoor attack, Stealthiness, Sensitivity mask optimization