# Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior

arXiv: https://arxiv.org/abs/2003.07637 · HTML: https://mlsanity.com/paper/2003.07637/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations](https://mlsanity.com/paper/2110.01823/) (2021)
- [Boosting the Transferability of Video Adversarial Examples via Temporal Translation](https://mlsanity.com/paper/2110.09075/) (2021)
- [Efficient Decision-based Black-box Patch Attacks on Video Recognition](https://mlsanity.com/paper/2303.11917/) (2023)
- [Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos](https://mlsanity.com/paper/2301.00896/) (2023)
- [Attacking Video Recognition Models with Bullet-Screen Comments](https://mlsanity.com/paper/2110.15629/) (2021)
- [Physical Adversarial Attacks for Surveillance: A Survey](https://mlsanity.com/paper/2305.01074/) (2023)
- [GAMA: Generative Adversarial Multi-Object Scene Attacks](https://mlsanity.com/paper/2209.09502/) (2022)
- [Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency](https://mlsanity.com/paper/2108.09891/) (2021)
- [Exploiting Vision Encoder Vulnerabilities for Universal Adversarial Perturbations on Large Vision-Language Models](https://mlsanity.com/paper/2412.08108/) (2024)
- [Defending Black-box Skeleton-based Human Activity Classifiers](https://mlsanity.com/paper/2203.04713/) (2022)
- [Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems](https://mlsanity.com/paper/2204.03141/) (2022)
- [Breaking Temporal Consistency: Generating Video Universal Adversarial Perturbations Using Image Models](https://mlsanity.com/paper/2311.10366/) (2023)
- [Semantically Stealthy Adversarial Attacks against Segmentation Models](https://mlsanity.com/paper/2104.01732/) (2021)
- [VideoPure: Diffusion-based Adversarial Purification for Video Recognition](https://mlsanity.com/paper/2501.14999/) (2025)
- [Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition](https://mlsanity.com/paper/2211.13171/) (2022)
- [Robustness Evaluation for Video Models with Reinforcement Learning](https://mlsanity.com/paper/2506.05431/) (2025)

## Abstract (arXiv)

Deep neural networks are known to be susceptible to adversarial noise, which are tiny and imperceptible perturbations. Most of previous work on adversarial attack mainly focus on image models, while the vulnerability of video models is less explored. In this paper, we aim to attack video models by utilizing intrinsic movement pattern and regional relative motion among video frames. We propose an effective motion-excited sampler to obtain motion-aware noise prior, which we term as sparked prior. Our sparked prior underlines frame correlations and utilizes video dynamics via relative motion. By using the sparked prior in gradient estimation, we can successfully attack a variety of video classification models with fewer number of queries. Extensive experimental results on four benchmark datasets validate the efficacy of our proposed method.