# Appending Adversarial Frames for Universal Video Attack

arXiv: https://arxiv.org/abs/1912.04538 · HTML: https://mlsanity.com/paper/1912.04538/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

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

- [A Survey On Universal Adversarial Attack](https://mlsanity.com/paper/2103.01498/) (2021)
- [Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations](https://mlsanity.com/paper/2110.01823/) (2021)
- [StyleFool: Fooling Video Classification Systems via Style Transfer](https://mlsanity.com/paper/2203.16000/) (2022)
- [MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes](https://mlsanity.com/paper/2103.14211/) (2021)
- [Attacking Video Recognition Models with Bullet-Screen Comments](https://mlsanity.com/paper/2110.15629/) (2021)
- [Sparse Adversarial Video Attacks with Spatial Transformations](https://mlsanity.com/paper/2111.05468/) (2021)
- [Adversarial Bone Length Attack on Action Recognition](https://mlsanity.com/paper/2109.05830/) (2021)
- [Two-Face: Adversarial Audit of Commercial Face Recognition Systems](https://mlsanity.com/paper/2111.09137/) (2021)
- [LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer](https://mlsanity.com/paper/2312.09935/) (2023)
- [Improve Video Representation with Temporal Adversarial Augmentation](https://mlsanity.com/paper/2304.14601/) (2023)
- [Query-Efficient Video Adversarial Attack with Stylized Logo on Service Computing](https://mlsanity.com/paper/2408.12099/) (2024)
- [LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model](https://mlsanity.com/paper/2403.11656/) (2024)
- [FrameShield: Adversarially Robust Video Anomaly Detection](https://mlsanity.com/paper/2510.21532/) (2025)
- [Inter-frame Accelerate Attack against Video Interpolation Models](https://mlsanity.com/paper/2305.06540/) (2023)
- [Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models](https://mlsanity.com/paper/2602.11244/) (2026)
- [Robustness Evaluation for Video Models with Reinforcement Learning](https://mlsanity.com/paper/2506.05431/) (2025)
- [DDSA: Dual-Domain Strategic Attack for Spatial-Temporal Efficiency in Adversarial Robustness Testing](https://mlsanity.com/paper/2601.14302/) (2026)

## Abstract (arXiv)

There have been many efforts in attacking image classification models with adversarial perturbations, but the same topic on video classification has not yet been thoroughly studied. This paper presents a novel idea of video-based attack, which appends a few dummy frames (e.g., containing the texts of `thanks for watching') to a video clip and then adds adversarial perturbations only on these new frames. Our approach enjoys three major benefits, namely, a high success rate, a low perceptibility, and a strong ability in transferring across different networks. These benefits mostly come from the common dummy frame which pushes all samples towards the boundary of classification. On the other hand, such attacks are easily to be concealed since most people would not notice the abnormality behind the perturbed video clips. We perform experiments on two popular datasets with six state-of-the-art video classification models, and demonstrate the effectiveness of our approach in the scenario of universal video attacks.