[2604.00422] Shapley-Guided Neural Repair Approach via Derivative-Free Optimization

Computer Science > Software Engineering arXiv:2604.00422 (cs) [Submitted on 1 Apr 2026] Title:Shapley-Guided Neural Repair Approach via Derivative-Free Optimization Authors:Xinyu Sun, Wanwei Liu, Haoang Chi, Tingyu Chen, Xiaoguang Mao, Shangwen Wang, Lei Bu, Jingyi Wang, Yang Tan, Zhenyi Qi View a PDF of the paper titled Shapley-Guided Neural Repair Approach via Derivative-Free Optimization, by Xinyu Sun and 9 other authors View PDF HTML (experimental) Abstract:DNNs are susceptible to defects like backdoors, adversarial attacks, and unfairness, undermining their reliability. Existing approaches mainly involve retraining, optimization, constraint-solving, or search algorithms. However, most methods rely on gradient calculations, restricting applicability to specific activation functions (e.g., ReLU), or use search algorithms with uninterpretable localization and repair. Furthermore, they often lack generalizability across multiple properties. We propose SHARPEN, integrating interpretable fault localization with a derivative-free optimization strategy. First, SHARPEN introduces a Deep SHAP-based localization strategy quantifying each layer's and neuron's marginal contribution to erroneous outputs. Specifically, a hierarchical coarse-to-fine approach reranks layers by aggregated impact, then locates faulty neurons/filters by analyzing activation divergences between property-violating and benign states. Subsequently, SHARPEN incorporates CMA-ES to repair identified neurons. CM...