# Trustworthy Anomaly Detection: A Survey

arXiv: https://arxiv.org/abs/2202.07787 · HTML: https://mlsanity.com/paper/2202.07787/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 65 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 30 of 34)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [To be Robust or to be Fair: Towards Fairness in Adversarial Training](https://mlsanity.com/paper/2010.06121/) (2020)
- [Explainable Deep One-Class Classification](https://mlsanity.com/paper/2007.01760/) (2020)
- [Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy](https://mlsanity.com/paper/1911.07116/) (2019)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Explainable Deep Few-shot Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/2108.00462/) (2021)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series](https://mlsanity.com/paper/2010.05073/) (2020)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Stakeholders in Explainable AI](https://mlsanity.com/paper/1810.00184/) (2018)
- [GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection](https://mlsanity.com/paper/1903.06661/) (2019)
- [Explaining Anomalies Detected by Autoencoders Using SHAP](https://mlsanity.com/paper/1903.02407/) (2019)
- [Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure](https://mlsanity.com/paper/2007.10088/) (2020)
- [Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models](https://mlsanity.com/paper/1805.06230/) (2018)
- [FairOD: Fairness-aware Outlier Detection](https://mlsanity.com/paper/2012.03063/) (2020)
- [Towards Fair Deep Anomaly Detection](https://mlsanity.com/paper/2012.14961/) (2020)
- [Adversarially Robust One-class Novelty Detection](https://mlsanity.com/paper/2108.11168/) (2021)
- [Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection](https://mlsanity.com/paper/1803.04967/) (2018)

## Built on by (most cited first, 5 of 5)

- [One-class Damage Detector Using Deeper Fully-Convolutional Data Descriptions for Civil Application](https://mlsanity.com/paper/2303.01732/) (2023)
- [Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability](https://mlsanity.com/paper/2404.06144/) (2024)
- [Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Review](https://mlsanity.com/paper/2411.14278/) (2024)
- [Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance](https://mlsanity.com/paper/2307.12676/) (2023)
- [Autonomous VR-Based Risk Detection for Situational Awareness in Dangerous Settings](https://mlsanity.com/paper/2607.16582/) (2026)

## Abstract (arXiv)

Anomaly detection has a wide range of real-world applications, such as bank fraud detection and cyber intrusion detection. In the past decade, a variety of anomaly detection models have been developed, which lead to big progress towards accurately detecting various anomalies. Despite the successes, anomaly detection models still face many limitations. The most significant one is whether we can trust the detection results from the models. In recent years, the research community has spent a great effort to design trustworthy machine learning models, such as developing trustworthy classification models. However, the attention to anomaly detection tasks is far from sufficient. Considering that many anomaly detection tasks are life-changing tasks involving human beings, labeling someone as anomalies or fraudsters should be extremely cautious. Hence, ensuring the anomaly detection models conducted in a trustworthy fashion is an essential requirement to deploy the models to conduct automatic decisions in the real world. In this brief survey, we summarize the existing efforts and discuss open problems towards trustworthy anomaly detection from the perspectives of interpretability, fairness, robustness, and privacy-preservation.