# Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE

arXiv: https://arxiv.org/abs/2406.14073 · HTML: https://mlsanity.com/paper/2406.14073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## 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)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks](https://mlsanity.com/paper/2110.03825/) (2021)
- [On Adversarial Robustness: A Neural Architecture Search perspective](https://mlsanity.com/paper/2007.08428/) (2020)
- [DENSER: Deep Evolutionary Network Structured Representation](https://mlsanity.com/paper/1801.01563/) (2018)
- [Understanding Robust Learning through the Lens of Representation Similarities](https://mlsanity.com/paper/2206.09868/) (2022)

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

- [LoBAM: LoRA-Based Backdoor Attack on Model Merging](https://mlsanity.com/paper/2411.16746/) (2024)

## Abstract (arXiv)

Adversarial examples, designed to trick Artificial Neural Networks (ANNs) into producing wrong outputs, highlight vulnerabilities in these models. Exploring these weaknesses is crucial for developing defenses, and so, we propose a method to assess the adversarial robustness of image-classifying ANNs. The t-distributed Stochastic Neighbor Embedding (t-SNE) technique is used for visual inspection, and a metric, which compares the clean and perturbed embeddings, helps pinpoint weak spots in the layers. Analyzing two ANNs on CIFAR-10, one designed by humans and another via NeuroEvolution, we found that differences between clean and perturbed representations emerge early on, in the feature extraction layers, affecting subsequent classification. The findings with our metric are supported by the visual analysis of the t-SNE maps.