# Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach

arXiv: https://arxiv.org/abs/2404.17020 · HTML: https://mlsanity.com/paper/2404.17020/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [GenAttack: Practical Black-box Attacks with Gradient-Free Optimization](https://mlsanity.com/paper/1805.11090/) (2018)
- [Accurate, reliable and fast robustness evaluation](https://mlsanity.com/paper/1907.01003/) (2019)
- [Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study](https://mlsanity.com/paper/1912.00805/) (2019)
- [When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study](https://mlsanity.com/paper/2212.11368/) (2022)

## Abstract (arXiv)

Deep Learning (DL) models excel in computer vision tasks but can be susceptible to adversarial examples. This paper introduces Triple-Metric EvoAttack (TM-EVO), an efficient algorithm for evaluating the robustness of object-detection DL models against adversarial attacks. TM-EVO utilizes a multi-metric fitness function to guide an evolutionary search efficiently in creating effective adversarial test inputs with minimal perturbations. We evaluate TM-EVO on widely-used object-detection DL models, DETR and Faster R-CNN, and open-source datasets, COCO and KITTI. Our findings reveal that TM-EVO outperforms the state-of-the-art EvoAttack baseline, leading to adversarial tests with less noise while maintaining efficiency.