# Adversarial Examples in Modern Machine Learning: A Review

arXiv: https://arxiv.org/abs/1911.05268 · HTML: https://mlsanity.com/paper/1911.05268/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 30 of 36)

- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies](https://mlsanity.com/paper/2203.14046/) (2022)
- [A Survey on Transferability of Adversarial Examples across Deep Neural Networks](https://mlsanity.com/paper/2310.17626/) (2023)
- [Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview](https://mlsanity.com/paper/2206.07438/) (2022)
- [A Survey on Physical Adversarial Attack in Computer Vision](https://mlsanity.com/paper/2209.14262/) (2022)
- [Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases](https://mlsanity.com/paper/2412.18295/) (2024)
- [Physically Consistent Neural Networks for building thermal modeling: theory and analysis](https://mlsanity.com/paper/2112.03212/) (2021)
- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [Physical Adversarial Attacks for Surveillance: A Survey](https://mlsanity.com/paper/2305.01074/) (2023)
- [On the Robustness of Bayesian Neural Networks to Adversarial Attacks](https://mlsanity.com/paper/2207.06154/) (2022)
- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey](https://mlsanity.com/paper/2307.00309/) (2023)
- [EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks](https://mlsanity.com/paper/2107.05754/) (2021)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive Denoising](https://mlsanity.com/paper/2107.05780/) (2021)
- [Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.07003/) (2023)
- [AdvFAS: A robust face anti-spoofing framework against adversarial examples](https://mlsanity.com/paper/2308.02116/) (2023)
- [Arabic Synonym BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2402.03477/) (2024)
- [RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering](https://mlsanity.com/paper/2411.09749/) (2024)
- [Comparison of semi-supervised deep learning algorithms for audio classification](https://mlsanity.com/paper/2102.08183/) (2021)
- [Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training](https://mlsanity.com/paper/2304.05492/) (2023)
- [Localized Uncertainty Attacks](https://mlsanity.com/paper/2106.09222/) (2021)
- [Improving Adversarial Robustness for Free with Snapshot Ensemble](https://mlsanity.com/paper/2110.03124/) (2021)
- [An Exploration of How Training Set Composition Bias in Machine Learning Affects Identifying Rare Objects](https://mlsanity.com/paper/2207.03207/) (2022)
- [An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models](https://mlsanity.com/paper/2208.07464/) (2022)
- [The Pros and Cons of Adversarial Robustness](https://mlsanity.com/paper/2312.10911/) (2023)
- [ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testing](https://mlsanity.com/paper/2411.00217/) (2024)
- [BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks](https://mlsanity.com/paper/2103.08031/) (2021)
- [Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification](https://mlsanity.com/paper/2203.07027/) (2022)
- [Detecting and Recovering Adversarial Examples from Extracting Non-robust and Highly Predictive Adversarial Perturbations](https://mlsanity.com/paper/2206.15128/) (2022)

## Abstract (arXiv)

Recent research has found that many families of machine learning models are vulnerable to adversarial examples: inputs that are specifically designed to cause the target model to produce erroneous outputs. In this survey, we focus on machine learning models in the visual domain, where methods for generating and detecting such examples have been most extensively studied. We explore a variety of adversarial attack methods that apply to image-space content, real world adversarial attacks, adversarial defenses, and the transferability property of adversarial examples. We also discuss strengths and weaknesses of various methods of adversarial attack and defense. Our aim is to provide an extensive coverage of the field, furnishing the reader with an intuitive understanding of the mechanics of adversarial attack and defense mechanisms and enlarging the community of researchers studying this fundamental set of problems.