# Learning to Adversarially Blur Visual Object Tracking

arXiv: https://arxiv.org/abs/2107.12085 · HTML: https://mlsanity.com/paper/2107.12085/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 55 references that are in the map.
- First posted on arXiv 26 Jul 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [Generating Adversarial Examples with Adversarial Networks](https://mlsanity.com/paper/1801.02610/) (2018)
- [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020)
- [Distractor-aware Siamese Networks for Visual Object Tracking](https://mlsanity.com/paper/1808.06048/) (2018)
- [Siam R-CNN: Visual Tracking by Re-Detection](https://mlsanity.com/paper/1911.12836/) (2019)
- [TEA: Temporal Excitation and Aggregation for Action Recognition](https://mlsanity.com/paper/2004.01398/) (2020)
- [Deeper and Wider Siamese Networks for Real-Time Visual Tracking](https://mlsanity.com/paper/1901.01660/) (2019)
- [Know Your Surroundings: Exploiting Scene Information for Object Tracking](https://mlsanity.com/paper/2003.11014/) (2020)
- [Multi-Modal Domain Adaptation for Fine-Grained Action Recognition](https://mlsanity.com/paper/2001.09691/) (2020)
- [High-Performance Long-Term Tracking with Meta-Updater](https://mlsanity.com/paper/2004.00305/) (2020)
- [Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking](https://mlsanity.com/paper/1812.06148/) (2018)
- [Learning to Synthesize Motion Blur](https://mlsanity.com/paper/1811.11745/) (2018)
- [Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises](https://mlsanity.com/paper/2003.09595/) (2020)
- [Physical Adversarial Textures that Fool Visual Object Tracking](https://mlsanity.com/paper/1904.11042/) (2019)
- [Motion Deblurring in the Wild](https://mlsanity.com/paper/1701.01486/) (2017)
- [Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack](https://mlsanity.com/paper/2007.07097/) (2020)

## Built on by (most cited first, 14 of 14)

- [Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory](https://mlsanity.com/paper/2403.12445/) (2024)
- [Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack](https://mlsanity.com/paper/2411.02669/) (2024)
- [ALA: Naturalness-aware Adversarial Lightness Attack](https://mlsanity.com/paper/2201.06070/) (2022)
- [ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks](https://mlsanity.com/paper/2111.13330/) (2021)
- [Benchmarking Shadow Removal for Facial Landmark Detection and Beyond](https://mlsanity.com/paper/2111.13790/) (2021)
- [Masked Faces with Faced Masks](https://mlsanity.com/paper/2201.06427/) (2022)
- [MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models](https://mlsanity.com/paper/2509.08538/) (2025)
- [A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems](https://mlsanity.com/paper/2307.06659/) (2023)
- [LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks](https://mlsanity.com/paper/2404.06247/) (2024)
- [Learning Motion Blur Robust Vision Transformers for Real-Time UAV Tracking](https://mlsanity.com/paper/2407.05383/) (2024)
- [Towards Adaptive Meta-Gradient Adversarial Examples for Visual Tracking](https://mlsanity.com/paper/2505.08999/) (2025)
- [Adversarial Attack for RGB-Event based Visual Object Tracking](https://mlsanity.com/paper/2504.14423/) (2025)
- [AngleRoCL: Angle-Robust Concept Learning for Physically View-Invariant T2I Adversarial Patches](https://mlsanity.com/paper/2506.09538/) (2025)
- [NAPPure: Adversarial Purification for Robust Image Classification under Non-Additive Perturbations](https://mlsanity.com/paper/2510.14025/) (2025)

## Abstract (arXiv)

Motion blur caused by the moving of the object or camera during the exposure can be a key challenge for visual object tracking, affecting tracking accuracy significantly. In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i.e., adversarial blur attack (ABA). Our main objective is to online transfer input frames to their natural motion-blurred counterparts while misleading the state-of-the-art trackers during the tracking process. To this end, we first design the motion blur synthesizing method for visual tracking based on the generation principle of motion blur, considering the motion information and the light accumulation process. With this synthetic method, we propose optimization-based ABA (OP-ABA) by iteratively optimizing an adversarial objective function against the tracking w.r.t. the motion and light accumulation parameters. The OP-ABA is able to produce natural adversarial examples but the iteration can cause heavy time cost, making it unsuitable for attacking real-time trackers. To alleviate this issue, we further propose one-step ABA (OS-ABA) where we design and train a joint adversarial motion and accumulation predictive network (JAMANet) with the guidance of OP-ABA, which is able to efficiently estimate the adversarial motion and accumulation parameters in a one-step way. The experiments on four popular datasets (e.g., OTB100, VOT2018, UAV123, and LaSOT) demonstrate that our methods are able to cause significant accuracy drops on four state-of-the-art trackers with high transferability. Please find the source code at urlhttps://github.com/tsingqguo/ABA.