论文标题
部分可观测时空混沌系统的无模型预测
Empirical Evaluation of Physical Adversarial Patch Attacks Against Overhead Object Detection Models
论文作者
论文摘要
对抗贴片是旨在欺骗其他表现良好的基于神经网络的计算机视觉模型的图像。尽管这些攻击最初是通过数字方式构想和研究的，但由于图像的原始像素值受到干扰，但最近的工作表明，这些攻击可以成功地转移到物理世界中。可以通过打印补丁并将其添加到新捕获的图像或视频镜头的场景中来实现。在这项工作中，我们进一步测试了在更具挑战性的条件下物理世界中对抗斑块攻击的功效。我们考虑通过空中或卫星摄像机获得的高架图像训练的对象检测模型，并测试插入沙漠环境场景中的物理对抗斑块。我们的主要发现是，在这些条件下，成功实施对抗贴剂攻击要比在先前考虑的条件下更难。这对AI安全具有重要意义，因为可能被夸大了对抗性例子所带来的现实世界威胁。
Adversarial patches are images designed to fool otherwise well-performing neural network-based computer vision models. Although these attacks were initially conceived of and studied digitally, in that the raw pixel values of the image were perturbed, recent work has demonstrated that these attacks can successfully transfer to the physical world. This can be accomplished by printing out the patch and adding it into scenes of newly captured images or video footage. In this work we further test the efficacy of adversarial patch attacks in the physical world under more challenging conditions. We consider object detection models trained on overhead imagery acquired through aerial or satellite cameras, and we test physical adversarial patches inserted into scenes of a desert environment. Our main finding is that it is far more difficult to successfully implement the adversarial patch attacks under these conditions than in the previously considered conditions. This has important implications for AI safety as the real-world threat posed by adversarial examples may be overstated.
