# Distracting Downpour: Adversarial Weather Attacks for Motion Estimation

arXiv: https://arxiv.org/abs/2305.06716 · HTML: https://mlsanity.com/paper/2305.06716/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 57 references that are in the map.
- First posted on arXiv 11 May 2023.

## Builds on (most cited first, 30 of 34)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://mlsanity.com/paper/1907.07484/) (2019)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo](https://mlsanity.com/paper/2303.01943/) (2023)
- [Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon](https://mlsanity.com/paper/2203.03818/) (2022)
- [Rain rendering for evaluating and improving robustness to bad weather](https://mlsanity.com/paper/2009.03683/) (2020)
- [Physics-Based Rendering for Improving Robustness to Rain](https://mlsanity.com/paper/1908.10335/) (2019)
- [Attacking Optical Flow](https://mlsanity.com/paper/1910.10053/) (2019)
- [When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks](https://mlsanity.com/paper/2105.06152/) (2021)
- [Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation](https://mlsanity.com/paper/2103.13660/) (2021)
- [AdvHaze: Adversarial Haze Attack](https://mlsanity.com/paper/2104.13673/) (2021)
- [CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks](https://mlsanity.com/paper/2302.02213/) (2023)
- [Optical Flow in Dense Foggy Scenes using Semi-Supervised Learning](https://mlsanity.com/paper/2004.01905/) (2020)
- [Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations](https://mlsanity.com/paper/2009.10142/) (2020)
- [Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networks](https://mlsanity.com/paper/2103.05422/) (2021)
- [Stereoscopic Universal Perturbations across Different Architectures and Datasets](https://mlsanity.com/paper/2112.06116/) (2021)
- [A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow](https://mlsanity.com/paper/2203.13214/) (2022)
- [Towards Understanding Adversarial Robustness of Optical Flow Networks](https://mlsanity.com/paper/2103.16255/) (2021)
- [Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method](https://mlsanity.com/paper/1911.08790/) (2019)
- [Adversarial Rain Attack and Defensive Deraining for DNN Perception](https://mlsanity.com/paper/2009.09205/) (2020)
- [Attacking Motion Estimation with Adversarial Snow](https://mlsanity.com/paper/2210.11242/) (2022)
- [fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems](https://mlsanity.com/paper/2205.13807/) (2022)

## Built on by (most cited first, 15 of 15)

- [A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks](https://mlsanity.com/paper/2310.00633/) (2023)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Controllable Weather Synthesis and Removal with Video Diffusion Models](https://mlsanity.com/paper/2505.00704/) (2025)
- [Adversarial Examples in the Physical World: A Survey](https://mlsanity.com/paper/2311.01473/) (2023)
- [Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context](https://mlsanity.com/paper/2311.17524/) (2023)
- [RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo](https://mlsanity.com/paper/2505.09368/) (2025)
- [Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow](https://mlsanity.com/paper/2310.17403/) (2023)
- [Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation](https://mlsanity.com/paper/2409.17001/) (2024)
- [RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering](https://mlsanity.com/paper/2411.09749/) (2024)
- [Towards Dependable Optical Flow: Benchmarks for Real-World Spatiotemporal Corruptions](https://mlsanity.com/paper/2411.14865/) (2024)
- [CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection](https://mlsanity.com/paper/2403.18554/) (2024)
- [Position: Towards Resilience Against Adversarial Examples](https://mlsanity.com/paper/2405.01349/) (2024)
- [Unveiling Hidden Vulnerabilities in Digital Human Generation via Adversarial Attacks](https://mlsanity.com/paper/2504.17457/) (2025)
- [LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2505.12009/) (2025)
- [AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models](https://mlsanity.com/paper/2607.06485/) (2026)

## Abstract (arXiv)

Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, adverse weather conditions constitute a much more realistic threat scenario. Hence, in this work, we present a novel attack on motion estimation that exploits adversarially optimized particles to mimic weather effects like snowflakes, rain streaks or fog clouds. At the core of our attack framework is a differentiable particle rendering system that integrates particles (i) consistently over multiple time steps (ii) into the 3D space (iii) with a photo-realistic appearance. Through optimization, we obtain adversarial weather that significantly impacts the motion estimation. Surprisingly, methods that previously showed good robustness towards small per-pixel perturbations are particularly vulnerable to adversarial weather. At the same time, augmenting the training with non-optimized weather increases a method's robustness towards weather effects and improves generalizability at almost no additional cost. Our code will be available at https://github.com/cv-stuttgart/DistractingDownpour.