论文标题
蝴蝶效应攻击：对象检测的微小且看似无关的扰动
Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection
论文作者
论文摘要
这项工作旨在探索和确定对象检测中图像的微小且看似无关的扰动，这将导致性能退化。虽然可以使用$ L_P $规范自然定义微小，但我们通过扰动和对象之间的像素距离和对象之间的像素距离来表征对象的“无关”程度。在满足两个目标的同时触发错误，可以作为多目标优化问题进行表述，在该问题中，我们利用遗传算法来指导搜索。结果成功地证明了图像右侧（无形的）扰动可以大大改变左侧对象检测的结果。广泛的评估重申了我们的猜想，与单阶段对象检测网络（如Yolov5）相比，基于变压器的对象检测网络更容易受到蝴蝶效应的影响。
This work aims to explore and identify tiny and seemingly unrelated perturbations of images in object detection that will lead to performance degradation. While tininess can naturally be defined using $L_p$ norms, we characterize the degree of "unrelatedness" of an object by the pixel distance between the occurred perturbation and the object. Triggering errors in prediction while satisfying two objectives can be formulated as a multi-objective optimization problem where we utilize genetic algorithms to guide the search. The result successfully demonstrates that (invisible) perturbations on the right part of the image can drastically change the outcome of object detection on the left. An extensive evaluation reaffirms our conjecture that transformer-based object detection networks are more susceptible to butterfly effects in comparison to single-stage object detection networks such as YOLOv5.
