# Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

arXiv: https://arxiv.org/abs/2204.03141 · HTML: https://mlsanity.com/paper/2204.03141/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 25 of 25)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Adversarially Learned One-Class Classifier for Novelty Detection](https://mlsanity.com/paper/1802.09088/) (2018)
- [Anomaly Detection in Video Sequence with Appearance-Motion Correspondence](https://mlsanity.com/paper/1908.06351/) (2019)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [Street Scene: A new dataset and evaluation protocol for video anomaly detection](https://mlsanity.com/paper/1902.05872/) (2019)
- [Learning Deep Representations of Appearance and Motion for Anomalous Event Detection](https://mlsanity.com/paper/1510.01553/) (2015)
- [Black-box Adversarial Attacks on Video Recognition Models](https://mlsanity.com/paper/1904.05181/) (2019)
- [Sparse Adversarial Perturbations for Videos](https://mlsanity.com/paper/1803.02536/) (2018)
- [Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge](https://mlsanity.com/paper/1709.09121/) (2017)
- [Continual Learning for Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2004.07941/) (2020)
- [Heuristic Black-box Adversarial Attacks on Video Recognition Models](https://mlsanity.com/paper/1911.09449/) (2019)
- [Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks](https://mlsanity.com/paper/2002.05123/) (2020)
- [Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds](https://mlsanity.com/paper/1706.07680/) (2017)
- [Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations](https://mlsanity.com/paper/2110.01823/) (2021)
- [Sparse Black-box Video Attack with Reinforcement Learning](https://mlsanity.com/paper/2001.03754/) (2020)
- [Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate](https://mlsanity.com/paper/2010.07110/) (2020)
- [Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior](https://mlsanity.com/paper/2003.07637/) (2020)
- [Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers](https://mlsanity.com/paper/1811.11875/) (2018)
- [A Modular and Unified Framework for Detecting and Localizing Video Anomalies](https://mlsanity.com/paper/2103.11299/) (2021)

## Built on by (most cited first, 9 of 9)

- [Cross-Domain Video Anomaly Detection without Target Domain Adaptation](https://mlsanity.com/paper/2212.07010/) (2022)
- [ComplexVAD: Detecting Interaction Anomalies in Video](https://mlsanity.com/paper/2501.09733/) (2025)
- [Multimodal Attack Detection for Action Recognition Models](https://mlsanity.com/paper/2404.10790/) (2024)
- [Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis](https://mlsanity.com/paper/2305.19593/) (2023)
- [Detecting Adversarial Examples](https://mlsanity.com/paper/2410.17442/) (2024)
- [Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers](https://mlsanity.com/paper/2506.20816/) (2025)
- [Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning](https://mlsanity.com/paper/2403.08806/) (2024)
- [Robustness of Agentic AI Systems via Adversarially-Aligned Jacobian Regularization](https://mlsanity.com/paper/2603.04378/) (2026)
- [Detecting Adversarial Data via Provable Adversarial Noise Amplification](https://mlsanity.com/paper/2605.02109/) (2026)

## Abstract (arXiv)

Anomaly detection in videos is an important computer vision problem with various applications including automated video surveillance. Although adversarial attacks on image understanding models have been heavily investigated, there is not much work on adversarial machine learning targeting video understanding models and no previous work which focuses on video anomaly detection. To this end, we investigate an adversarial machine learning attack against video anomaly detection systems, that can be implemented via an easy-to-perform cyber-attack. Since surveillance cameras are usually connected to the server running the anomaly detection model through a wireless network, they are prone to cyber-attacks targeting the wireless connection. We demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform and effective denial-of-service (DoS) attack, can be utilized to generate adversarial data for video anomaly detection systems. Specifically, we apply several effects caused by the Wi-Fi deauthentication attack on video quality (e.g., slow down, freeze, fast forward, low resolution) to the popular benchmark datasets for video anomaly detection. Our experiments with several state-of-the-art anomaly detection models show that the attackers can significantly undermine the reliability of video anomaly detection systems by causing frequent false alarms and hiding physical anomalies from the surveillance system.