# Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors

arXiv: https://arxiv.org/abs/2412.18815 · HTML: https://mlsanity.com/paper/2412.18815/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 22 of 22)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Physical Adversarial Examples for Object Detectors](https://mlsanity.com/paper/1807.07769/) (2018)
- [EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples](https://mlsanity.com/paper/1709.04114/) (2017)
- [DPatch: An Adversarial Patch Attack on Object Detectors](https://mlsanity.com/paper/1806.02299/) (2018)
- [Adversarial Examples that Fool Detectors](https://mlsanity.com/paper/1712.02494/) (2017)
- [ADef: an Iterative Algorithm to Construct Adversarial Deformations](https://mlsanity.com/paper/1804.07729/) (2018)
- [Physical Adversarial Attacks on an Aerial Imagery Object Detector](https://mlsanity.com/paper/2108.11765/) (2021)
- [Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios](https://mlsanity.com/paper/2202.04781/) (2022)
- [The Level Weighted Structural Similarity Loss: A Step Away from the MSE](https://mlsanity.com/paper/1904.13362/) (2019)
- [Real-time 3D Semantic Scene Perception for Egocentric Robots with Binocular Vision](https://mlsanity.com/paper/2402.11872/) (2024)
- [V3D-SLAM: Robust RGB-D SLAM in Dynamic Environments with 3D Semantic Geometry Voting](https://mlsanity.com/paper/2410.12068/) (2024)
- [Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots](https://mlsanity.com/paper/2412.11241/) (2024)
- [On the Efficacy of Metrics to Describe Adversarial Attacks](https://mlsanity.com/paper/2301.13028/) (2023)

## Built on by (most cited first, 1 of 1)

- [Robustness Evaluation of OCR-based Visual Document Understanding under Multi-Modal Adversarial Attacks](https://mlsanity.com/paper/2506.16407/) (2025)

## Abstract (arXiv)

Deep learning-based object detection has become ubiquitous in the last decade due to its high accuracy in many real-world applications. With this growing trend, these models are interested in being attacked by adversaries, with most of the results being on classifiers, which do not match the context of practical object detection. In this work, we propose a novel method to fool object detectors, expose the vulnerability of state-of-the-art detectors, and promote later works to build more robust detectors to adversarial examples. Our method aims to generate adversarial images by perturbing object confidence scores during training, which is crucial in predicting confidence for each class in the testing phase. Herein, we provide a more intuitive technique to embed additive noises based on detected objects' masks and the training loss with distortion control over the original image by leveraging the gradient of iterative images. To verify the proposed method, we perform adversarial attacks against different object detectors, including the most recent state-of-the-art models like YOLOv8, Faster R-CNN, RetinaNet, and Swin Transformer. We also evaluate our technique on MS COCO 2017 and PASCAL VOC 2012 datasets and analyze the trade-off between success attack rate and image distortion. Our experiments show that the achievable success attack rate is up to 100% and up to 98% when performing white-box and black-box attacks, respectively. The source code and relevant documentation for this work are available at the following link: https://github.com/anonymous20210106/attackdetector