# Multiclass ASMA vs Targeted PGD Attack in Image Segmentation

arXiv: https://arxiv.org/abs/2208.01844 · HTML: https://mlsanity.com/paper/2208.01844/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation](https://mlsanity.com/paper/1907.13124/) (2019)

## Built on by (most cited first, 2 of 2)

- [CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks](https://mlsanity.com/paper/2302.02213/) (2023)
- [Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning](https://mlsanity.com/paper/2305.11347/) (2023)

## Abstract (arXiv)

Deep learning networks have demonstrated high performance in a large variety of applications, such as image classification, speech recognition, and natural language processing. However, there exists a major vulnerability exploited by the use of adversarial attacks. An adversarial attack imputes images by altering the input image very slightly, making it nearly undetectable to the naked eye, but results in a very different classification by the network. This paper explores the projected gradient descent (PGD) attack and the Adaptive Mask Segmentation Attack (ASMA) on the image segmentation DeepLabV3 model using two types of architectures: MobileNetV3 and ResNet50, It was found that PGD was very consistent in changing the segmentation to be its target while the generalization of ASMA to a multiclass target was not as effective. The existence of such attack however puts all of image classification deep learning networks in danger of exploitation.