# Towards Adversarially Robust Object Detection

arXiv: https://arxiv.org/abs/1907.10310 · HTML: https://mlsanity.com/paper/1907.10310/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 30 of 55)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021)
- [Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization](https://mlsanity.com/paper/2112.12376/) (2021)
- [Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection](https://mlsanity.com/paper/2112.04532/) (2021)
- [X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection](https://mlsanity.com/paper/2302.09491/) (2023)
- [Robust and Accurate Object Detection via Adversarial Learning](https://mlsanity.com/paper/2103.13886/) (2021)
- [COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts](https://mlsanity.com/paper/2307.12730/) (2023)
- [PAD: Patch-Agnostic Defense against Adversarial Patch Attacks](https://mlsanity.com/paper/2404.16452/) (2024)
- [DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks](https://mlsanity.com/paper/2102.02956/) (2021)
- [Class-Aware Robust Adversarial Training for Object Detection](https://mlsanity.com/paper/2103.16148/) (2021)
- [ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking](https://mlsanity.com/paper/2202.01811/) (2022)
- [Object Detection with Transformers: A Review](https://mlsanity.com/paper/2306.04670/) (2023)
- [Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios](https://mlsanity.com/paper/2202.04781/) (2022)
- [Does Robustness on ImageNet Transfer to Downstream Tasks?](https://mlsanity.com/paper/2204.03934/) (2022)
- [Adversarially-Aware Robust Object Detector](https://mlsanity.com/paper/2207.06202/) (2022)
- [Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks](https://mlsanity.com/paper/2301.13487/) (2023)
- [Identifying Label Errors in Object Detection Datasets by Loss Inspection](https://mlsanity.com/paper/2303.06999/) (2023)
- [Towards Robust General Medical Image Segmentation](https://mlsanity.com/paper/2107.04263/) (2021)
- [Test-Time Backdoor Detection for Object Detection Models](https://mlsanity.com/paper/2503.15293/) (2025)
- [On the Importance of Backbone to the Adversarial Robustness of Object Detectors](https://mlsanity.com/paper/2305.17438/) (2023)
- [Robust Real-World Image Super-Resolution against Adversarial Attacks](https://mlsanity.com/paper/2208.00428/) (2022)
- [Generalized Real-World Super-Resolution through Adversarial Robustness](https://mlsanity.com/paper/2108.11505/) (2021)
- [Natural Adversarial Objects](https://mlsanity.com/paper/2111.04204/) (2021)
- [A Survey and Evaluation of Adversarial Attacks for Object Detection](https://mlsanity.com/paper/2408.01934/) (2024)
- [Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models](https://mlsanity.com/paper/2109.06363/) (2021)
- [Robust Backdoor Attacks on Object Detection in Real World](https://mlsanity.com/paper/2309.08953/) (2023)
- [Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks](https://mlsanity.com/paper/2406.05857/) (2024)
- [Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training](https://mlsanity.com/paper/2411.02149/) (2024)
- [Consistent Semantic Attacks on Optical Flow](https://mlsanity.com/paper/2111.08485/) (2021)
- [Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection](https://mlsanity.com/paper/2206.01736/) (2022)

## Abstract (arXiv)

Object detection is an important vision task and has emerged as an indispensable component in many vision system, rendering its robustness as an increasingly important performance factor for practical applications. While object detection models have been demonstrated to be vulnerable against adversarial attacks by many recent works, very few efforts have been devoted to improving their robustness. In this work, we take an initial attempt towards this direction. We first revisit and systematically analyze object detectors and many recently developed attacks from the perspective of model robustness. We then present a multi-task learning perspective of object detection and identify an asymmetric role of task losses. We further develop an adversarial training approach which can leverage the multiple sources of attacks for improving the robustness of detection models. Extensive experiments on PASCAL-VOC and MS-COCO verified the effectiveness of the proposed approach.