重新思考图像缩放攻击：机器学习系统中漏洞之间的相互作用
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
摘要：
由于真实世界的图像大小不同，因此机器学习模型是包括上游图像缩放算法的更大系统的一部分。在本文中，我们调查的图像缩放过程和机器学习模型在基于决策的黑盒设置的漏洞之间的相互作用。我们提出了一种新的采样策略，使黑盒攻击利用漏洞的缩放算法，缩放防御，并最终机器学习模型在端到端的方式。基于这种伸缩感知攻击，我们揭示了大多数现有的伸缩防御在下游模型的威胁下是无效的。此外，我们根据经验观察到，标准的黑盒攻击可以显着提高其性能，利用脆弱的缩放过程。我们进一步证明了这个问题的商业图像分析API与决策为基础的黑盒攻击。
英文摘要：
As real-world images come in varying sizes, the machine learning model is part of a larger system that includes an upstream image scaling algorithm. In this paper, we investigate the interplay between vulnerabilities of the image scaling procedure and machine learning models in the decision-based black-box setting. We propose a novel sampling strategy to make a black-box attack exploit vulnerabilities in scaling algorithms, scaling defenses, and the final machine learning model in an end-to-end manner. Based on this scaling-aware attack, we reveal that most existing scaling defenses are ineffective under threat from downstream models. Moreover, we empirically observe that standard black-box attacks can significantly improve their performance by exploiting the vulnerable scaling procedure. We further demonstrate this problem on a commercial Image Analysis API with decision-based black-box attacks.
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