# UVL2: A Unified Framework for Video Tampering Localization

arXiv: https://arxiv.org/abs/2309.16126 · HTML: https://mlsanity.com/paper/2309.16126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [Learning Joint Spatial-Temporal Transformations for Video Inpainting](https://mlsanity.com/paper/2007.10247/) (2020)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [Deep Flow-Guided Video Inpainting](https://mlsanity.com/paper/1905.02884/) (2019)
- [Deep Video Inpainting](https://mlsanity.com/paper/1905.01639/) (2019)
- [Flow-edge Guided Video Completion](https://mlsanity.com/paper/2009.01835/) (2020)
- [Copy-and-Paste Networks for Deep Video Inpainting](https://mlsanity.com/paper/1908.11587/) (2019)
- [Onion-Peel Networks for Deep Video Completion](https://mlsanity.com/paper/1908.08718/) (2019)
- [Generate, Segment and Refine: Towards Generic Manipulation Segmentation](https://mlsanity.com/paper/1811.09729/) (2018)
- [MetaFormer: A Unified Meta Framework for Fine-Grained Recognition](https://mlsanity.com/paper/2203.02751/) (2022)
- [Deep Video Inpainting Detection](https://mlsanity.com/paper/2101.11080/) (2021)
- [Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering](https://mlsanity.com/paper/2102.01439/) (2021)
- [Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos](https://mlsanity.com/paper/2112.08117/) (2021)

## Built on by (most cited first, 1 of 1)

- [RelayFormer: A Unified Local-Global Attention Framework for Scalable Image and Video Manipulation Localization](https://mlsanity.com/paper/2508.09459/) (2025)

## Abstract (arXiv)

With the advancement of deep learning-driven video editing technology, security risks have emerged. Malicious video tampering can lead to public misunderstanding, property losses, and legal disputes. Currently, detection methods are mostly limited to specific datasets, with limited detection performance for unknown forgeries, and lack of robustness for processed data. This paper proposes an effective video tampering localization network that significantly improves the detection performance of video inpainting and splicing by extracting more generalized features of forgery traces. Considering the inherent differences between tampered videos and original videos, such as edge artifacts, pixel distribution, texture features, and compress information, we have specifically designed four modules to independently extract these features. Furthermore, to seamlessly integrate these features, we employ a two-stage approach utilizing both a Convolutional Neural Network and a Vision Transformer, enabling us to learn these features in a local-to-global manner. Experimental results demonstrate that the method significantly outperforms the existing state-of-the-art methods and exhibits robustness.