# Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers

arXiv: https://arxiv.org/abs/2411.17468 · HTML: https://mlsanity.com/paper/2411.17468/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Robust Object Modeling for Visual Tracking](https://mlsanity.com/paper/2308.05140/) (2023)
- [Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises](https://mlsanity.com/paper/2003.09595/) (2020)
- [Robust Tracking against Adversarial Attacks](https://mlsanity.com/paper/2007.09919/) (2020)
- [IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking](https://mlsanity.com/paper/2103.14938/) (2021)
- [High-Performance Transformer Tracking](https://mlsanity.com/paper/2203.13533/) (2022)
- [Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers](https://mlsanity.com/paper/2406.01765/) (2024)

## 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)

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS.