arXiv subjects

Hongbo Yu

Publications and source records attributed to Hongbo Yu.

3 recordsLinked to original sources

arXiv2026

Cryptanalytic Extraction of Neural Networks Without Known Architecture Assumption

Cryptanalytic extraction attacks recover the parameters of a neural network given only black-box access to its raw output. However, all existing attacks rely on a fundamental assumption: the attacker knows the network architecture. For example, regarding ReLU activation-based fully connected networks, the network depth and the dimension of each hidden layer are known. In this paper, we study whether this assumption can be removed. We focus on ReLU fully connected networks and propose a guess-and-determine framework that recovers the architecture and the parameters jointly. The core of our approach is a simple but powerful observation: dimension guessing leaves architecture-sensitive traces in the parameter recovery process. We identify two such traces: (i) a \emph{zero suffix} in the merged weight vectors produced by signature recovery, whose length reveals the number of excess guesses; and (ii) an \emph{equality pattern} in the preimage-based sign recovery, which occurs only when the dimension guess is correct. These two signals give rise to two complementary recovery routes. We further propose two criteria for identifying the second-to-last layer, which is necessary for terminating the guessing process. We implement end-to-end attacks on a wide range of ReLU networks, including both expansive and non-expansive architectures. To the best of our knowledge, this is the first cryptanalytic extraction attack that removes the assumption of known network architecture.

cs.CR↗

arXiv2026

PhysSFI-Net: Physics-informed Geometric Learning of Skeletal and Facial Interactions for Orthognathic Surgical Outcome Prediction

Orthognathic surgery repositions jaw bones to restore occlusion and enhance facial aesthetics. Accurate simulation of postoperative facial morphology is essential for preoperative planning. This study aims to develop and validate a physics-informed geometric deep learning framework named PhysSFI-Net for precise prediction of soft tissue deformation following orthognathic surgery. The model integrates a hierarchical feature extraction module with attention mechanisms to capture skeletal-facial interactions, an LSTM-based sequential predictor for incremental deformation, and a biomechanics-inspired reconstruction module for high-resolution facial modeling. The model was trained on 135 patients and externally validated on an independent cohort of 33 patients. Model performance was assessed using point cloud shape error, surface deviation error and landmark error between predicted facial shapes with corresponding ground truths. Quantitative analysis demonstrated that PhysSFI-Net achieved a global shape error of 1.070 +/- 0.088 mm, a surface deviation error of 1.296 +/- 0.349 mm and a landmark error of 2.445 +/- 1.326 mm. Comparative experiments indicated that PhysSFI-Net outperformed the state-of-the-art method ACMT-Net and baseline models. External validation further confirmed its robustness with a global HD of 1.431 +/- 0.087 mm and consistently lower subregional and mesh-based errors. In conclusion, PhysSFI-Net enables interpretable, high-resolution prediction of postoperative facial morphology, showing strong potential for clinical application in orthognathic surgical planning.

cs.CV↗

arXiv2025

Delving into Cryptanalytic Extraction of PReLU Neural Networks

The machine learning problem of model extraction was first introduced in 1991 and gained prominence as a cryptanalytic challenge starting with Crypto 2020. For over three decades, research in this field has primarily focused on ReLU-based neural networks. In this work, we take the first step towards the cryptanalytic extraction of PReLU neural networks, which employ more complex nonlinear activation functions than their ReLU counterparts. We propose a raw output-based parameter recovery attack for PReLU networks and extend it to more restrictive scenarios where only the top-m probability scores are accessible. Our attacks are rigorously evaluated through end-to-end experiments on diverse PReLU neural networks, including models trained on the MNIST dataset. To the best of our knowledge, this is the first practical demonstration of PReLU neural network extraction across three distinct attack scenarios.

cs.CR↗