# AI-GAN: Attack-Inspired Generation of Adversarial Examples

arXiv: https://arxiv.org/abs/2002.02196 · HTML: https://mlsanity.com/paper/2002.02196/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 22 of 22)

- [Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations](https://mlsanity.com/paper/2202.08602/) (2022)
- [Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator](https://mlsanity.com/paper/2208.06222/) (2022)
- [Generating Universal Adversarial Perturbations for Quantum Classifiers](https://mlsanity.com/paper/2402.08648/) (2024)
- [Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks](https://mlsanity.com/paper/2107.14776/) (2021)
- [Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models](https://mlsanity.com/paper/2308.15692/) (2023)
- [Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification](https://mlsanity.com/paper/2312.07338/) (2023)
- [Evading Detection Actively: Toward Anti-Forensics against Forgery Localization](https://mlsanity.com/paper/2310.10036/) (2023)
- [SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledge](https://mlsanity.com/paper/2402.14937/) (2024)
- [Deep generative models as an adversarial attack strategy for tabular machine learning](https://mlsanity.com/paper/2409.12642/) (2024)
- [Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition](https://mlsanity.com/paper/2106.04392/) (2021)
- [Using a GAN to Generate Adversarial Examples to Facial Image Recognition](https://mlsanity.com/paper/2111.15213/) (2021)
- [Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter](https://mlsanity.com/paper/2309.09443/) (2023)
- [GMTR: Graph Matching Transformers](https://mlsanity.com/paper/2311.08141/) (2023)
- [DarkShot: Lighting Dark Images with Low-Compute and High-Quality](https://mlsanity.com/paper/2312.16805/) (2023)
- [TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing](https://mlsanity.com/paper/2401.05431/) (2024)
- [TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks](https://mlsanity.com/paper/2401.05432/) (2024)
- [T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Image](https://mlsanity.com/paper/2403.13663/) (2024)
- [Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithms](https://mlsanity.com/paper/2408.17101/) (2024)
- [Towards General Deepfake Detection with Dynamic Curriculum](https://mlsanity.com/paper/2410.11162/) (2024)
- [Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2410.13409/) (2024)
- [Mask6D: Masked Pose Priors For 6D Object Pose Estimation](https://mlsanity.com/paper/2507.06486/) (2025)
- [Attack Assessment and Augmented Identity Recognition for Human Skeleton Data](https://mlsanity.com/paper/2603.24232/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs. Recently different attacks and strategies have been proposed, but how to generate adversarial examples perceptually realistic and more efficiently remains unsolved. This paper proposes a novel framework called Attack-Inspired GAN (AI-GAN), where a generator, a discriminator, and an attacker are trained jointly. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. Through extensive experiments on several popular datasets eg MNIST and CIFAR-10, AI-GAN achieves high attack success rates and reduces generation time significantly in various settings. Moreover, for the first time, AI-GAN successfully scales to complicated datasets eg CIFAR-100 with around 90% success rates among all classes.