Variational Autoencoder-Based Black-Box Adversarial Attack on Collaborative DNN Inference

TL;DR

This paper introduces AdVAR-DNN, a black-box adversarial attack leveraging variational autoencoders to compromise collaborative DNN inference in IoT environments, exposing privacy vulnerabilities.

Contribution

It presents a novel VAE-based attack method that effectively targets collaborative DNN inference without prior model knowledge.

Findings

High attack success rate on popular DNNs

Effective in untraceable sample generation

Minimal detection probability

Abstract

In recent years, Deep Neural Networks (DNNs) have become increasingly integral to IoT-based environments, enabling realtime visual computing. However, the limited computational capacity of these devices has motivated the adoption of collaborative DNN inference, where the IoT device offloads part of the inference-related computation to a remote server. Such offloading often requires dynamic DNN partitioning information to be exchanged among the participants over an unsecured network or via relays/hops, leading to novel privacy vulnerabilities. In this paper, we propose AdVAR-DNN, an adversarial variational autoencoder (VAE)-based misclassification attack, leveraging classifiers to detect model information and a VAE to generate untraceable manipulated samples, specifically designed to compromise the collaborative inference process. AdVAR-DNN attack uses the sensitive information exchange vulnerability of collaborative DNN inference and is black-box in nature in terms of having no prior knowledge about the DNN model and how it is partitioned. Our evaluation using the most popular object classification DNNs on the CIFAR-100 dataset demonstrates the effectiveness of AdVAR-DNN in terms of high attack success rate with little to no probability of detection.