# Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis

arXiv: https://arxiv.org/abs/2408.10021 · HTML: https://mlsanity.com/paper/2408.10021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 41 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 30 of 32)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [Detecting Adversarial Samples from Artifacts](https://mlsanity.com/paper/1703.00410/) (2017)
- [On the Robustness of Semantic Segmentation Models to Adversarial Attacks](https://mlsanity.com/paper/1711.09856/) (2017)
- [Universal Adversarial Perturbations Against Semantic Image Segmentation](https://mlsanity.com/paper/1704.05712/) (2017)
- [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022)
- [SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness](https://mlsanity.com/paper/2207.12391/) (2022)
- [Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks](https://mlsanity.com/paper/2108.06179/) (2021)
- [Houdini: Fooling Deep Structured Prediction Models](https://mlsanity.com/paper/1707.05373/) (2017)
- [Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation](https://mlsanity.com/paper/1810.05162/) (2018)
- [Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation](https://mlsanity.com/paper/2003.06555/) (2020)
- [Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent](https://mlsanity.com/paper/2106.15023/) (2021)
- [Cubic LSTMs for Video Prediction](https://mlsanity.com/paper/1904.09412/) (2019)
- [CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks](https://mlsanity.com/paper/2302.02213/) (2023)
- [Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation](https://mlsanity.com/paper/2004.11072/) (2020)
- [Indirect Local Attacks for Context-aware Semantic Segmentation Networks](https://mlsanity.com/paper/1911.13038/) (2019)
- [Proximal Splitting Adversarial Attacks for Semantic Segmentation](https://mlsanity.com/paper/2206.07179/) (2022)
- [Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks](https://mlsanity.com/paper/1911.05075/) (2019)
- [Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation](https://mlsanity.com/paper/2209.05980/) (2022)
- [DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation](https://mlsanity.com/paper/1908.05195/) (2019)
- [The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing](https://mlsanity.com/paper/2101.03924/) (2021)
- [Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation](https://mlsanity.com/paper/2310.17436/) (2023)
- [Semantically Stealthy Adversarial Attacks against Segmentation Models](https://mlsanity.com/paper/2104.01732/) (2021)

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

- [Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation](https://mlsanity.com/paper/2606.09536/) (2026)

## Abstract (arXiv)

Deep neural networks have demonstrated remarkable effectiveness across a wide range of tasks such as semantic segmentation. Nevertheless, these networks are vulnerable to adversarial attacks that add imperceptible perturbations to the input image, leading to false predictions. This vulnerability is particularly dangerous in safety-critical applications like automated driving. While adversarial examples and defense strategies are well-researched in the context of image classification, there is comparatively less research focused on semantic segmentation. Recently, we have proposed an uncertainty-based method for detecting adversarial attacks on neural networks for semantic segmentation. We observed that uncertainty, as measured by the entropy of the output distribution, behaves differently on clean versus adversely perturbed images, and we utilize this property to differentiate between the two. In this extended version of our work, we conduct a detailed analysis of uncertainty-based detection of adversarial attacks including a diverse set of adversarial attacks and various state-of-the-art neural networks. Our numerical experiments show the effectiveness of the proposed uncertainty-based detection method, which is lightweight and operates as a post-processing step, i.e., no model modifications or knowledge of the adversarial example generation process are required.