There has been a rise in the use of Machine Learning as a Service (MLaaS)
Vision APIs as they offer multiple services including pre-built models and
algorithms, which otherwise take a huge amount of resources if built from
scratch. As these APIs get deployed for high-stakes applications, it's very
important that they are robust to different manipulations. Recent works have
only focused on typical adversarial attacks when evaluating the robustness of
vision APIs. We propose two new aspects of adversarial image generation methods
and evaluate them on the robustness of Google Cloud Vision API's optical
character recognition service and object detection APIs deployed in real-world
settings such as sightengine.com, picpurify.com, Google Cloud Vision API, and
Microsoft Azure's Computer Vision API. Specifically, we go beyond the
conventional small-noise adversarial attacks and introduce secret embedding and
transparent adversarial examples as a simpler way to evaluate robustness. These
methods are so straightforward that even non-specialists can craft such
attacks. As a result, they pose a serious threat where APIs are used for
high-stakes applications. Our transparent adversarial examples successfully
evade state-of-the art object detections APIs such as Azure Cloud Vision
(attack success rate 52%) and Google Cloud Vision (attack success rate 36%).
90% of the images have a secret embedded text that successfully fools the
vision of time-limited humans but is detected by Google Cloud Vision API's
optical character recognition. Complementing to current research, our results
provide simple but unconventional methods on robustness evaluation.

通过引入秘密嵌入和透明对抗性示例作为一种更简单的评估鲁棒性的方法，本文在实际部署的 Google Cloud Vision API 和 Microsoft Azure 的 Computer Vision API 等场景下评估其恶意攻击的成功率，结果表明这种方法非常容易使人产生视觉上的欺骗，并能成功逃避现有的最先进 API，为当前研究提供一种补充的鲁棒性评估方法。