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This work aims to explore and identify tiny and seemingly unrelatedperturbations of images in object detection that will lead to performancedegradation. While tininess can naturally be defined using $L_p$ norms, wecharacterize the degree of "unrelatedness" of an object by the pixel distancebetween the occurred perturbation and the object. Triggering errors inprediction while satisfying two objectives can be formulated as amulti-objective optimization problem where we utilize genetic algorithms toguide the search. The result successfully demonstrates that (invisible)perturbations on the right part of the image can drastically change the outcomeof object detection on the left. An extensive evaluation reaffirms ourconjecture that transformer-based object detection networks are moresusceptible to butterfly effects in comparison to single-stage object detectionnetworks such as YOLOv5.

Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection