# Eigenpatches -- Adversarial Patches from Principal Components

arXiv: https://arxiv.org/abs/2306.10963 · HTML: https://mlsanity.com/paper/2306.10963/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 19 Jun 2023.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [The Space of Transferable Adversarial Examples](https://mlsanity.com/paper/1704.03453/) (2017)
- [Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors](https://mlsanity.com/paper/1910.14667/) (2019)
- [Are adversarial examples inevitable?](https://mlsanity.com/paper/1809.02104/) (2018)
- [Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World](https://mlsanity.com/paper/2205.05909/) (2022)
- [Jedi: Entropy-based Localization and Removal of Adversarial Patches](https://mlsanity.com/paper/2304.10029/) (2023)
- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [Learning Coated Adversarial Camouflages for Object Detectors](https://mlsanity.com/paper/2109.00124/) (2021)
- [Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature](https://mlsanity.com/paper/2305.01361/) (2023)
- [How many dimensions are required to find an adversarial example?](https://mlsanity.com/paper/2303.14173/) (2023)
- [Origins of Low-dimensional Adversarial Perturbations](https://mlsanity.com/paper/2203.13779/) (2022)
- [A Tutorial on Adversarial Learning Attacks and Countermeasures](https://mlsanity.com/paper/2202.10377/) (2022)
- [Energy Attack: On Transferring Adversarial Examples](https://mlsanity.com/paper/2109.04300/) (2021)

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

- [Traversing the Subspace of Adversarial Patches](https://mlsanity.com/paper/2412.01527/) (2024)

## Abstract (arXiv)

Adversarial patches are still a simple yet powerful white box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector. This paper addresses the problem of computational expensiveness by analyzing 375 generated patches, calculating the principal components of these and show, that linear combinations of the resulting "eigenpatches" can be used to fool object detections successfully.