# Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances

arXiv: https://arxiv.org/abs/2111.07035 · HTML: https://mlsanity.com/paper/2111.07035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Improving Adversarial Robustness via Promoting Ensemble Diversity](https://mlsanity.com/paper/1901.08846/) (2019)
- [Technical Report on the CleverHans v2.1.0 Adversarial Examples Library](https://mlsanity.com/paper/1610.00768/) (2016)
- [Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics](https://mlsanity.com/paper/1612.07767/) (2016)
- [Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness](https://mlsanity.com/paper/1908.11091/) (2019)
- [Training Ensembles to Detect Adversarial Examples](https://mlsanity.com/paper/1712.04006/) (2017)
- [Adversarial Examples Detection and Analysis with Layer-wise Autoencoders](https://mlsanity.com/paper/2006.10013/) (2020)
- [Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks](https://mlsanity.com/paper/2005.08321/) (2020)
- [Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?](https://mlsanity.com/paper/2010.15974/) (2020)

## Abstract (arXiv)

Deep learning models have been used for a wide variety of tasks. They are prevalent in computer vision, natural language processing, speech recognition, and other areas. While these models have worked well under many scenarios, it has been shown that they are vulnerable to adversarial attacks. This has led to a proliferation of research into ways that such attacks could be identified and/or defended against. Our goal is to explore the contribution that can be attributed to using multiple underlying models for the purpose of adversarial instance detection. Our paper describes two approaches that incorporate representations from multiple models for detecting adversarial examples. We devise controlled experiments for measuring the detection impact of incrementally utilizing additional models. For many of the scenarios we consider, the results show that performance increases with the number of underlying models used for extracting representations.