HuffDuff: Stealing Pruned DNNs from Sparse Accelerators
Dingqing Yang, Prashant J. Nair, Mieszko Lis · 2023
Deep learning models are a valuable “secret sauce” that confers a significant competitive advantage. Many models are never visible to the user and even publicly known state-of-the-art models are either completely proprietary or only accessible via access-controlled APIs. Increasingly, these models run directly on the edge, often using a low-power DNN accelerator. This makes models particularly vulnerable, as an attacker with physical access can exploit side channels like off-chip memory access volumes. Indeed, prior work has shown that this channel can be used to steal dense DNNs from edge devices by correlating data transfer volumes with layer geometry.