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.

翻译：模型窃取攻击已成为深度学习模型的严重安全隐患，攻击者可通过查询黑盒API窃取已训练模型，导致知识产权盗窃及其他安全与隐私风险。当前最先进的防御策略建议对预测概率添加扰动，但这些方法存在计算开销大、对攻击者假设不切实际等问题，且通常需要训练辅助模型。这不仅耗时耗力，更阻碍了防御措施在实际场景中的部署。本文提出一种简单、高效且有效的防御方案：我们引入启发式方法对输出概率进行扰动。该防御无需额外训练即可轻松集成至现有模型。实验证明，所提方法能有效抵御三种主流窃取攻击。我们在多个视觉数据集上训练的大规模及量化（压缩）卷积神经网络上评估该方案，其性能优于现有最先进防御方法，推理延迟降低37倍，无需额外模型且对模型性能影响极小。我们验证了该方法对面向边缘设备的量化CNN同样有效。