# Multi-Task Consistency-based Detection of Adversarial Attacks

arXiv: https://arxiv.org/abs/2608.07750 · HTML: https://mlsanity.com/paper/2608.07750/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 7 Aug 2026.

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

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory](https://mlsanity.com/paper/1609.02132/) (2016)
- [ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector](https://mlsanity.com/paper/1804.05810/) (2018)
- [Adversarial Robustness vs Model Compression, or Both?](https://mlsanity.com/paper/1903.12561/) (2019)
- [Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification](https://mlsanity.com/paper/1611.05377/) (2016)
- [When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks](https://mlsanity.com/paper/1911.10695/) (2019)
- [PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier](https://mlsanity.com/paper/2108.09135/) (2021)
- [Towards Adversarially Robust Object Detection](https://mlsanity.com/paper/1907.10310/) (2019)
- [SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2008.04017/) (2020)
- [A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies](https://mlsanity.com/paper/2203.14046/) (2022)
- [Detection based Defense against Adversarial Examples from the Steganalysis Point of View](https://mlsanity.com/paper/1806.09186/) (2018)
- [Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain](https://mlsanity.com/paper/2103.04302/) (2021)
- [Class-Aware Robust Adversarial Training for Object Detection](https://mlsanity.com/paper/2103.16148/) (2021)
- [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)
- [DLA: Dense-Layer-Analysis for Adversarial Example Detection](https://mlsanity.com/paper/1911.01921/) (2019)
- [Adversarially-Aware Robust Object Detector](https://mlsanity.com/paper/2207.06202/) (2022)
- [Adversarial Robustness in Multi-Task Learning: Promises and Illusions](https://mlsanity.com/paper/2110.15053/) (2021)
- [Detecting Adversarial Perturbations in Multi-Task Perception](https://mlsanity.com/paper/2203.01177/) (2022)
- [PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses](https://mlsanity.com/paper/2310.13076/) (2023)
- [Multi-Task Adversarial Attack](https://mlsanity.com/paper/2011.09824/) (2020)
- [UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks](https://mlsanity.com/paper/2108.04584/) (2021)
- [Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training](https://mlsanity.com/paper/1803.05123/) (2018)

## Abstract (arXiv)

Deep Neural Networks (DNNs) have found successful deployment in numerous vision perception systems. However, their susceptibility to adversarial attacks has prompted concerns regarding their practical applications, specifically in the context of autonomous driving. Existing defenses often suffer from cost inefficiency, rendering their deployment impractical for resource-constrained applications. In this work, we propose an efficient and effective adversarial attack detection scheme leveraging the multi-task perception within a complex vision system. Adversarial perturbations are detected by the inconsistencies between the inference outputs of multiple vision tasks, e.g., object detection and instance segmentation. To this end, we developed a consistency score metric to measure the inconsistency between vision tasks. Next, we designed an approach to select the best model pairs for detecting inconsistencies effectively. Finally, we evaluated our defense against PGD attacks across multiple vision models on the BDD100k validation dataset. The experimental results demonstrated that our defense achieved a ROC-AUC performance of 99.9% detection within the considered attacker model.