Fatemeh Nourilenjan Nokabadi

TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers

Fatemeh Nourilenjan Nokabadi

Yann Batiste Pequignot

Jean-Francois Lalonde

Christian Gagné

2025-05-27

Proceedings of the Conference on Robots and Vision (publié)

doi.org

openreview.net

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

Fatemeh Nourilenjan Nokabadi

Jean-Francois Lalonde

Christian Gagné

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (voir plus) 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.

2024-11-26

ArXiv (prépublication)

arxiv.org

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

Fatemeh Nourilenjan Nokabadi

Jean-Francois Lalonde

Christian Gagné

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (voir plus) 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.

2024-11-26

ArXiv (prépublication)

doi.org

arxiv.org

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

Fatemeh Nourilenjan Nokabadi

Jean-Francois Lalonde

Christian Gagné

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks… (voir plus) 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.

2024-10-15

NeurIPS.cc/2024/Workshop/AdvML-Frontiers (publié)

doi.org

openreview.net

TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers

Fatemeh Nourilenjan Nokabadi

Yann Batiste Pequignot

Jean-Francois Lalonde

Christian Gagné

Adversarial perturbations can deceive neural networks by adding small, imperceptible noise to the input. Recent object trackers with transfo… (voir plus)rmer backbones have shown strong performance on tracking datasets, but their adversarial robustness has not been thoroughly evaluated. While transformer trackers are resilient to black-box attacks, existing white-box adversarial attacks are not universally applicable against these new transformer trackers due to differences in backbone architecture. In this work, we introduce TrackPGD, a novel white-box attack that utilizes predicted object binary masks to target robust transformer trackers. Built upon the powerful segmentation attack SegPGD, our proposed TrackPGD effectively influences the decisions of transformer-based trackers. Our method addresses two primary challenges in adapting a segmentation attack for trackers: limited class numbers and extreme pixel class imbalance. TrackPGD uses the same number of iterations as other attack methods for tracker networks and produces competitive adversarial examples that mislead transformer and non-transformer trackers such as MixFormerM, OSTrackSTS, TransT-SEG, and RTS on datasets including VOT2022STS, DAVIS2016, UAV123, and GOT-10k.

2024-10-15

NeurIPS.cc/2024/Workshop/AdvML-Frontiers (publié)

openreview.net