{"ID":22952695,"CreatedAt":"2026-09-17T02:12:05.498442134Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.18751","arxiv_id":"2609.18751","title":"Normal Alignment: Improved Cryptanalytic Sign Recovery on Hard-Label Networks","abstract":"At EUROCRYPT 2025, Carlini et al. proposed a breakthrough in the cryptanalytic extraction on hard-label (S1) deep neural networks (DNNs), demonstrating polynomial-time signature and sign recovery. However, Carlini et al.'s sign-recovery method (which we call Future Toggle) suffers only a marginal advantage over random guessing, producing high-confidence wrong sign predictions in deeper layers. Such errors trigger expensive exponential-time enumeration. This work presents Normal Alignment, a novel statistical sign-recovery approach for S1 DNNs. Drawing on the expected length difference between projected normals of adjacent decision facets at dual points, our method infers neuron signs via normal-signature alignment. It delivers higher voting accuracy and pushes erroneous predictions to low-confidence ranks, which further enables a more efficient combined method, eSOE + Alignment, by combining Normal Alignment with the hard-label SOE extension. This combined strategy removes heavy enumeration overhead and realizes exact polynomial-time full sign recovery. Experiments demonstrate the effectiveness of our method, especially for deep layers. For example, with our method, the signs for CIFAR-10 (architecture 192-64$\\times$8-10) and MNIST (architecture 64-96$\\times$3-32-10) models can be fully recovered in polynomial time; in contrast, Carlini et al.'s sign-recovery method would require exponential-time enumerations involving $2^{52}$ or $2^{82}$ guesses of the signs, respectively.","short_abstract":"At EUROCRYPT 2025, Carlini et al. proposed a breakthrough in the cryptanalytic extraction on hard-label (S1) deep neural networks (DNNs), demonstrating polynomial-time signature and sign recovery. However, Carlini et al.'s sign-recovery method (which we call Future Toggle) suffers only a marginal advantage over random...","url_abs":"https://arxiv.org/abs/2609.18751","url_pdf":"https://arxiv.org/pdf/2609.18751v1","authors":"[\"Shi Tang\",\"Zirui Chen\",\"Yongjia Su\",\"Zhengchao Gao\",\"Lingyue Qin\",\"Xiaoyang Dong\"]","published":"2026-09-16T14:42:58Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[]","has_code":false}