PriRepVGG: Privacy-Preserving 3-Party Inference Framework for Image-Based Defect Detection

Abstract

Image classification is widely used in industrial defect detection, medical diagnosis, social welfare, and other fields, in which privacy and security of models and data must be involved. For example, in diamond synthesis, the diamond substrate image annotation data and the defect detection model are of value for conservation. Based on ensuring inference efficiency and the security of these private data intellectual property, the 3-party secure inference based on secure multi-party computation (MPC) can be adopted. MPC allows parties to use neural networks while preserving their input privacy for collaborative computing, but it will lead to huge communication and memory consumption. This paper propose PriRepVGG, a lightweight privacy-preserving image-based defect detection framework for 3-party. In this work, firstly, This work optimized the division and added an AdaptiveAvgpool layer in MPC framework FALCON; then, This work ported the inference architecture of the RegVGG network into FALCON creatively. Our work applied PriRepVGG to the secure inference of the diamond substrates defect detection under the data server, model server, and compute server settings, which can be carried out in batches with a low misjudgment rate and verify the feasibility of image-based secure inference with a lightweight network in an industrial case under MPC.