Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks

TL;DR

This paper presents a fast, simple, and resource-efficient heuristic method to defend CNNs against model stealing attacks by perturbing output probabilities, outperforming existing defenses in speed and practicality.

Contribution

The authors introduce a novel heuristic perturbation approach that requires no additional training and significantly improves defense speed and efficiency against model stealing.

Findings

Outperforms state-of-the-art defenses in speed by 37 times

Effective against multiple advanced stealing attacks

Works well on quantized CNNs for edge deployment

Abstract

Model stealing attacks have become a serious concern for deep learning models, where an attacker can steal a trained model by querying its black-box API. This can lead to intellectual property theft and other security and privacy risks. The current state-of-the-art defenses against model stealing attacks suggest adding perturbations to the prediction probabilities. However, they suffer from heavy computations and make impracticable assumptions about the adversary. They often require the training of auxiliary models. This can be time-consuming and resource-intensive which hinders the deployment of these defenses in real-world applications. In this paper, we propose a simple yet effective and efficient defense alternative. We introduce a heuristic approach to perturb the output probabilities. The proposed defense can be easily integrated into models without additional training. We show that our defense is effective in defending against three state-of-the-art stealing attacks. We evaluate our approach on large and quantized (i.e., compressed) Convolutional Neural Networks (CNNs) trained on several vision datasets. Our technique outperforms the state-of-the-art defenses with a $\times37$ faster inference latency without requiring any additional model and with a low impact on the model's performance. We validate that our defense is also effective for quantized CNNs targeting edge devices.