# APE-GAN: Adversarial Perturbation Elimination with GAN

arXiv: https://arxiv.org/abs/1707.05474 · HTML: https://mlsanity.com/paper/1707.05474/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2017.

## Built on by (most cited first, 30 of 57)

- [Relating Adversarially Robust Generalization to Flat Minima](https://mlsanity.com/paper/2104.04448/) (2021)
- [Ensembling with Deep Generative Views](https://mlsanity.com/paper/2104.14551/) (2021)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [Towards quantum enhanced adversarial robustness in machine learning](https://mlsanity.com/paper/2306.12688/) (2023)
- [Towards Defending against Adversarial Examples via Attack-Invariant Features](https://mlsanity.com/paper/2106.05036/) (2021)
- [Restricted Black-box Adversarial Attack Against DeepFake Face Swapping](https://mlsanity.com/paper/2204.12347/) (2022)
- [Unrestricted Adversarial Attacks on ImageNet Competition](https://mlsanity.com/paper/2110.09903/) (2021)
- [Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics](https://mlsanity.com/paper/2106.02749/) (2021)
- [Removing Adversarial Noise in Class Activation Feature Space](https://mlsanity.com/paper/2104.09197/) (2021)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Defenses in Adversarial Machine Learning: A Survey](https://mlsanity.com/paper/2312.08890/) (2023)
- [Improving Adversarial Robustness via Mutual Information Estimation](https://mlsanity.com/paper/2207.12203/) (2022)
- [Local Competition and Stochasticity for Adversarial Robustness in Deep Learning](https://mlsanity.com/paper/2101.01121/) (2021)
- [Analysis and Extensions of Adversarial Training for Video Classification](https://mlsanity.com/paper/2206.07953/) (2022)
- [Modeling Adversarial Noise for Adversarial Training](https://mlsanity.com/paper/2109.09901/) (2021)
- [Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks](https://mlsanity.com/paper/2204.12050/) (2022)
- [Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization](https://mlsanity.com/paper/2410.03376/) (2024)
- [Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator](https://mlsanity.com/paper/2208.06222/) (2022)
- [Adversarial Sampling for Fairness Testing in Deep Neural Network](https://mlsanity.com/paper/2303.02874/) (2023)
- [Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey](https://mlsanity.com/paper/2303.06302/) (2023)
- [Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks](https://mlsanity.com/paper/2404.03340/) (2024)
- [General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments](https://mlsanity.com/paper/2212.05387/) (2022)
- [Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training](https://mlsanity.com/paper/2106.05453/) (2021)
- [Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay](https://mlsanity.com/paper/2404.01828/) (2024)
- [On the role of feedback in visual processing: a predictive coding perspective](https://mlsanity.com/paper/2106.04225/) (2021)
- [Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification](https://mlsanity.com/paper/2312.07338/) (2023)
- [VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise](https://mlsanity.com/paper/2406.03117/) (2024)
- [Robustness Feature Adapter for Efficient Adversarial Training](https://mlsanity.com/paper/2508.17680/) (2025)
- [CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification](https://mlsanity.com/paper/2102.07304/) (2021)
- [Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN](https://mlsanity.com/paper/2103.04513/) (2021)

## Abstract (arXiv)

Although neural networks could achieve state-of-the-art performance while recongnizing images, they often suffer a tremendous defeat from adversarial examples--inputs generated by utilizing imperceptible but intentional perturbation to clean samples from the datasets. How to defense against adversarial examples is an important problem which is well worth researching. So far, very few methods have provided a significant defense to adversarial examples. In this paper, a novel idea is proposed and an effective framework based Generative Adversarial Nets named APE-GAN is implemented to defense against the adversarial examples. The experimental results on three benchmark datasets including MNIST, CIFAR10 and ImageNet indicate that APE-GAN is effective to resist adversarial examples generated from five attacks.