Exploring Adversarial Examples via Invertible Neural Networks

TL;DR

This paper introduces a novel approach using invertible neural networks to better understand, generate, and detect adversarial examples in deep learning, potentially improving robustness and training efficiency.

Contribution

It proposes using invertible neural models with Lipschitz continuity to analyze adversarial examples at a deeper level and develop faster generation and detection methods.

Findings

Enables in-depth analysis of adversarial examples via invertible models

Proposes a fast latent space adversarial example generation method

Suggests new avenues for adversarial example detection

Abstract

Adversarial examples (AEs) are images that can mislead deep neural network (DNN) classifiers via introducing slight perturbations into original images. This security vulnerability has led to vast research in recent years because it can introduce real-world threats into systems that rely on neural networks. Yet, a deep understanding of the characteristics of adversarial examples has remained elusive. We propose a new way of achieving such understanding through a recent development, namely, invertible neural models with Lipschitz continuous mapping functions from the input to the output. With the ability to invert any latent representation back to its corresponding input image, we can investigate adversarial examples at a deeper level and disentangle the adversarial example's latent representation. Given this new perspective, we propose a fast latent space adversarial example generation method that could accelerate adversarial training. Moreover, this new perspective could contribute to new ways of adversarial example detection.