# Synthetic Dataset Generation for Adversarial Machine Learning Research

arXiv: https://arxiv.org/abs/2207.10719 · HTML: https://mlsanity.com/paper/2207.10719/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 24 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 20 of 20)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [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)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?](https://mlsanity.com/paper/1610.01983/) (2016)
- [ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector](https://mlsanity.com/paper/1804.05810/) (2018)
- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/1707.03501/) (2017)
- [APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection](https://mlsanity.com/paper/1912.08166/) (2019)
- [ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches](https://mlsanity.com/paper/2203.04412/) (2022)
- [Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos](https://mlsanity.com/paper/1801.02780/) (2018)
- [Talk Proposal: Towards the Realistic Evaluation of Evasion Attacks using CARLA](https://mlsanity.com/paper/1904.12622/) (2019)
- [Defending Object Detectors against Patch Attacks with Out-of-Distribution Smoothing](https://mlsanity.com/paper/2205.08989/) (2022)

## Built on by (most cited first, 1 of 1)

- [REAP: A Large-Scale Realistic Adversarial Patch Benchmark](https://mlsanity.com/paper/2212.05680/) (2022)

## Abstract (arXiv)

Existing adversarial example research focuses on digitally inserted perturbations on top of existing natural image datasets. This construction of adversarial examples is not realistic because it may be difficult, or even impossible, for an attacker to deploy such an attack in the real-world due to sensing and environmental effects. To better understand adversarial examples against cyber-physical systems, we propose approximating the real-world through simulation. In this paper we describe our synthetic dataset generation tool that enables scalable collection of such a synthetic dataset with realistic adversarial examples. We use the CARLA simulator to collect such a dataset and demonstrate simulated attacks that undergo the same environmental transforms and processing as real-world images. Our tools have been used to collect datasets to help evaluate the efficacy of adversarial examples, and can be found at https://github.com/carla-simulator/carla/pull/4992.