# ODPure: Backdoor Purification for Object Detection via Ensemble Corruption Consensus

arXiv: https://arxiv.org/abs/2609.28239 · HTML: https://mlsanity.com/paper/2609.28239/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [Guided Diffusion Model for Adversarial Purification](https://mlsanity.com/paper/2205.14969/) (2022)
- [Robot Collapse: Supply Chain Backdoor Attacks Against VLM-based Robotic Manipulation](https://mlsanity.com/paper/2411.11683/) (2024)
- [Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning](https://mlsanity.com/paper/2304.10783/) (2023)
- [Image-to-Video Diffusion: From Foundations to Open Frontiers](https://mlsanity.com/paper/2605.17248/) (2026)
- [Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving](https://mlsanity.com/paper/2404.11357/) (2024)
- [TYPO: Instruction-Dense Visual Jailbreaks against Commercial Closed-Source Image-Generation Models](https://mlsanity.com/paper/2607.24897/) (2026)

## Abstract (arXiv)

With the development of applications like autonomous driving, object detection has gained significant attention, while also highlighting critical vulnerabilities like backdoor attacks that severely compromise model integrity. Specifically, such attacks involve altering the categories of objects (i.e., object misclassification), removing bounding boxes (i.e., object disappearance), or generating bounding box proposals for non-existent objects (i.e., object generation) when a predefined trigger is present in the input. Although backdoor defenses for image classification are well-established, the research for object detection remains comparatively underexplored. Existing defenses address these threats by scanning outputs or models for potential backdoors but require discarding either malicious data or models. This remedy fails to enable a continuous and accurate perceptual stream for the object detection pipeline. To address such limitations, we propose ODPure, a novel input-stage black-box defense for object detection, which is based on input purification that ensures stable perception flows. Tailored to the dense prediction nature of object detectors, our Corruption-Reconstruction-Selection (CRS) paradigm operates by neutralizing triggers through a diverse portfolio of corruptions to generate a massive pool of redundant proposals, then recovering fine-grained structural cues via generative priors, and finally employing voting to reach a consensus on the resulting detections. Comprehensive experiments demonstrate that our method provides robust defense against diverse backdoor attacks and trigger types while preserving baseline accuracy. Our code is available at https://github.com/Alex66366/ODPure.