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

Source: [Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection](<https://www.sota2.com/research/paper/68fa6de21e209131cea3ca76>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:45:11.347Z

Authors: Nguyen Anh Vu Doan, Arda Y\\"uksel, Chih-Hong Cheng

Published: 2022-11-14T16:07:14.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.07483>)

[PDF](<https://arxiv.org/pdf/2211.07483.pdf>)