Toward Adaptive Meta-Gradient Adversarial Examples for Visual Tracking

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F25%3A50022607" target="_blank" >RIV/62690094:18470/25:50022607 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/11051004" target="_blank" >https://ieeexplore.ieee.org/document/11051004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TR.2025.3569828" target="_blank" >10.1109/TR.2025.3569828</a>

Result language

angličtina

Original language name

Toward Adaptive Meta-Gradient Adversarial Examples for Visual Tracking

Original language description

In recent years, visual tracking methods based on convolutional neural networks and transformers have achieved remarkable performance and have been successfully applied in fields such as autonomous driving. However, the numerous security issues exposed by deep learning models have gradually affected the reliable application of visual tracking methods in real-world scenarios. Therefore, how to reveal the security vulnerabilities of existing visual trackers through effective adversarial attacks has become a critical problem that needs to be addressed. To this end, we propose an adaptive meta-gradient adversarial attack (AMGA) method for visual tracking. This method integrates multimodel ensemble and meta-learning strategies, combining momentum mechanisms and Gaussian smoothing, which can significantly enhance the transferability and attack effectiveness of adversarial examples. AMGA randomly selects models from a large model repository, constructs diverse tracking scenarios, and iteratively performs both white- and black-box adversarial attacks in each scenario, optimizing the gradient directions of each model. This paradigm minimizes the gap between white- and black-box adversarial attacks, thus achieving excellent attack performance in black-box scenarios. Extensive experimental results on large-scale datasets, such as OTB2015, LaSOT, and GOT-10 k demonstrate that AMGA significantly improves the attack performance, transferability, and deception of adversarial examples.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE TRANSACTIONS ON RELIABILITY

ISSN

0018-9529

e-ISSN

1558-1721

Volume of the periodical

74

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

5205-5218

UT code for WoS article

001518846000001

EID of the result in the Scopus database

2-s2.0-105009380337