With the recent increase in the computational power of modern mobile devices, machine learning-based heavy tasks such as face detection and speech recognition are now integral parts of such devices. This requires frameworks to execute machine learning models (e.g., Deep Neural Networks) on mobile devices. Although there exist studies on the accuracy and performance of these frameworks, the quality of on-device deep learning frameworks, in terms of their robustness, has not been systematically studied yet. In this paper, we empirically compare two on-device deep learning frameworks with three adversarial attacks on three different model architectures. We also use both the quantized and unquantized variants for each architecture. The results show that, in general, neither of the deep learning frameworks is better than the other in terms of robustness, and there is not a significant difference between the PC and mobile frameworks either. However, in cases like Boundary attack, mobile version is more robust than PC. In addition, quantization improves robustness in all cases when moving from PC to mobile.

中文翻译：

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移动平台上深度学习框架的稳健性分析

随着近来现代移动设备计算能力的增强，基于机器学习的繁重任务（例如人脸检测和语音识别）现在已成为此类设备的组成部分。这需要框架在移动设备上执行机器学习模型（例如，深度神经网络）。尽管存在关于这些框架的准确性和性能的研究，但尚未系统地研究设备端深度学习框架在鲁棒性方面的质量。在本文中，我们凭经验比较了两个设备端深度学习框架与针对三种不同模型架构的三种对抗性攻击。我们还为每个架构使用量化和未量化的变体。结果表明，一般来说，两种深度学习框架在鲁棒性方面都不优于另一种，PC 和移动框架之间也没有显着差异。但是，在边界攻击等情况下，移动版本比 PC 更健壮。此外，当从 PC 移动到移动设备时，量化提高了所有情况下的稳健性。