# Dominant Patterns: Critical Features Hidden in Deep Neural Networks

arXiv: https://arxiv.org/abs/2105.15057 · HTML: https://mlsanity.com/paper/2105.15057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 25 of 25)

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [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)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Generative Adversarial Perturbations](https://mlsanity.com/paper/1712.02328/) (2017)
- [Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations](https://mlsanity.com/paper/1801.08092/) (2018)
- [Fast Feature Fool: A data independent approach to universal adversarial perturbations](https://mlsanity.com/paper/1707.05572/) (2017)
- [Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations](https://mlsanity.com/paper/2007.06189/) (2020)
- [NAG: Network for Adversary Generation](https://mlsanity.com/paper/1712.03390/) (2017)
- [Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet](https://mlsanity.com/paper/2001.06325/) (2020)
- [Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions](https://mlsanity.com/paper/1808.01153/) (2018)
- [CD-UAP: Class Discriminative Universal Adversarial Perturbation](https://mlsanity.com/paper/2010.03300/) (2020)
- [Analysis of Dominant Classes in Universal Adversarial Perturbations](https://mlsanity.com/paper/2012.14352/) (2020)

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

- [FG-UAP: Feature-Gathering Universal Adversarial Perturbation](https://mlsanity.com/paper/2209.13113/) (2022)

## Abstract (arXiv)

In this paper, we find the existence of critical features hidden in Deep NeuralNetworks (DNNs), which are imperceptible but can actually dominate the outputof DNNs. We call these features dominant patterns. As the name suggests, for a natural image, if we add the dominant pattern of a DNN to it, the output of this DNN is determined by the dominant pattern instead of the original image, i.e., DNN's prediction is the same with the dominant pattern's. We design an algorithm to find such patterns by pursuing the insensitivity in the feature space. A direct application of the dominant patterns is the Universal Adversarial Perturbations(UAPs). Numerical experiments show that the found dominant patterns defeat state-of-the-art UAP methods, especially in label-free settings. In addition, dominant patterns are proved to have the potential to attack downstream tasks in which DNNs share the same backbone. We claim that DNN-specific dominant patterns reveal some essential properties of a DNN and are of great importance for its feature analysis and robustness enhancement.