Demo: Image Disguising for Scalable GPU-accelerated Confidential Deep Learning

Deep learning training involves large training data and expensive model tweaking, for which cloud GPU resources can be a popular option. However, outsourcing data often raises privacy concerns. The challenge is to preserve data and model confidentiality without sacrificing GPU-based scalable training and low-cost client-side preprocessing, which is difficult for conventional cryptographic solutions to achieve. This demonstration shows a new approach, image disguising, represented by recent work: DisguisedNets, NeuraCrypt, and InstaHide, which aim to securely transform training images while still enabling the desired scalability and efficiency. We present an interactive system for visually and comparatively exploring these methods. Users can view disguised images, note low client-side processing costs, and observe the maintained efficiency and model quality during server-side GPU-accelerated training. This demo aids researchers and practitioners in swiftly grasping the advantages and limitations of image-disguising methods.