# Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models

arXiv: https://arxiv.org/abs/2103.15897 · HTML: https://mlsanity.com/paper/2103.15897/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [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)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles](https://mlsanity.com/paper/2003.08757/) (2020)
- [Black Box to White Box: Discover Model Characteristics Based on Strategic Probing](https://mlsanity.com/paper/2009.03136/) (2020)
- [Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models](https://mlsanity.com/paper/2006.11130/) (2020)
- [Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks](https://mlsanity.com/paper/2102.09695/) (2021)

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

- [Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector](https://mlsanity.com/paper/2112.01723/) (2021)

## Abstract (arXiv)

Image classification is a common step in image recognition for machine learning in overhead applications. When applying popular model architectures like MobileNetV2, known vulnerabilities expose the model to counter-attacks, either mislabeling a known class or altering box location. This work proposes an automated approach to defend these models. We evaluate the use of multi-spectral image arrays and ensemble learners to combat adversarial attacks. The original contribution demonstrates the attack, proposes a remedy, and automates some key outcomes for protecting the model's predictions against adversaries. In rough analogy to defending cyber-networks, we combine techniques from both offensive ("red team") and defensive ("blue team") approaches, thus generating a hybrid protective outcome ("green team"). For machine learning, we demonstrate these methods with 3-color channels plus infrared for vehicles. The outcome uncovers vulnerabilities and corrects them with supplemental data inputs commonly found in overhead cases particularly.