# Spatio-temporal Co-attention Fusion Network for Video Splicing Localization

arXiv: https://arxiv.org/abs/2309.09482 · HTML: https://mlsanity.com/paper/2309.09482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation](https://mlsanity.com/paper/2106.05210/) (2021)
- [Attention to Scale: Scale-aware Semantic Image Segmentation](https://mlsanity.com/paper/1511.03339/) (2015)
- [See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks](https://mlsanity.com/paper/2001.06810/) (2020)
- [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023)
- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1804.00775/) (2018)
- [Attention-guided Temporally Coherent Video Object Matting](https://mlsanity.com/paper/2105.11427/) (2021)
- [Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusion](https://mlsanity.com/paper/2309.09306/) (2023)
- [Autoencoder with recurrent neural networks for video forgery detection](https://mlsanity.com/paper/1708.08754/) (2017)
- [EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle](https://mlsanity.com/paper/2210.08788/) (2022)
- [VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces](https://mlsanity.com/paper/2211.15775/) (2022)
- [Co-attention Propagation Network for Zero-Shot Video Object Segmentation](https://mlsanity.com/paper/2304.03910/) (2023)
- [Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis](https://mlsanity.com/paper/2207.13064/) (2022)
- [Effective Image Tampering Localization with Multi-Scale ConvNeXt Feature Fusion](https://mlsanity.com/paper/2208.13739/) (2022)

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

- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)

## Abstract (arXiv)

Digital video splicing has become easy and ubiquitous. Malicious users copy some regions of a video and paste them to another video for creating realistic forgeries. It is significant to blindly detect such forgery regions in videos. In this paper, a spatio-temporal co-attention fusion network (SCFNet) is proposed for video splicing localization. Specifically, a three-stream network is used as an encoder to capture manipulation traces across multiple frames. The deep interaction and fusion of spatio-temporal forensic features are achieved by the novel parallel and cross co-attention fusion modules. A lightweight multilayer perceptron (MLP) decoder is adopted to yield a pixel-level tampering localization map. A new large-scale video splicing dataset is created for training the SCFNet. Extensive tests on benchmark datasets show that the localization and generalization performances of our SCFNet outperform the state-of-the-art. Code and datasets will be available at https://github.com/multimediaFor/SCFNet.