# Structured Universal Adversarial Attacks on Object Detection for Video Sequences

arXiv: https://arxiv.org/abs/2510.14460 · HTML: https://mlsanity.com/paper/2510.14460/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder](https://mlsanity.com/paper/1905.09027/) (2019)
- [Mind the box: l1-APGD for sparse adversarial attacks on image classifiers](https://mlsanity.com/paper/2103.01208/) (2021)
- [Deep Multi-camera People Detection](https://mlsanity.com/paper/1702.04593/) (2017)
- [Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers](https://mlsanity.com/paper/2005.05321/) (2020)
- [RPATTACK: Refined Patch Attack on General Object Detectors](https://mlsanity.com/paper/2103.12469/) (2021)
- [Why do universal adversarial attacks work on large language models?: Geometry might be the answer](https://mlsanity.com/paper/2309.00254/) (2023)
- [Domain Generalization via Nuclear Norm Regularization](https://mlsanity.com/paper/2303.07527/) (2023)
- [Generating Adversarial Attacks in the Latent Space](https://mlsanity.com/paper/2304.04386/) (2023)
- [A Survey and Evaluation of Adversarial Attacks for Object Detection](https://mlsanity.com/paper/2408.01934/) (2024)
- [GSE: Group-wise Sparse and Explainable Adversarial Attacks](https://mlsanity.com/paper/2311.17434/) (2023)
- [Optimistic Optimisation of Composite Objective with Exponentiated Update](https://mlsanity.com/paper/2208.04065/) (2022)

## Abstract (arXiv)

Video-based object detection plays a vital role in safety-critical applications. While deep learning-based object detectors have achieved impressive performance, they remain vulnerable to adversarial attacks, particularly those involving universal perturbations. In this work, we propose a minimally distorted universal adversarial attack tailored for video object detection, which leverages nuclear norm regularization to promote structured perturbations concentrated in the background. To optimize this formulation efficiently, we employ an adaptive, optimistic exponentiated gradient method that enhances both scalability and convergence. Our results demonstrate that the proposed attack outperforms both low-rank projected gradient descent and Frank-Wolfe based attacks in effectiveness while maintaining high stealthiness. All code and data are publicly available at https://github.com/jsve96/AO-Exp-Attack.