# A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

arXiv: https://arxiv.org/abs/1703.04636 · HTML: https://mlsanity.com/paper/1703.04636/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2017.

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

- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues](https://mlsanity.com/paper/2212.14629/) (2022)
- [Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection](https://mlsanity.com/paper/2207.09135/) (2022)
- [NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection](https://mlsanity.com/paper/2306.06885/) (2023)
- [Learning Universal Features for Generalizable Image Forgery Localization](https://mlsanity.com/paper/2504.07462/) (2025)
- [AIGC Video Detection based on the fusion of spatial-frequency-optical flow multimodal features](https://mlsanity.com/paper/2609.26274/) (2026)
- [Automatic Localization and Detection Applicable to Robust Image Watermarking Resisting against Camera Shooting](https://mlsanity.com/paper/2304.13953/) (2023)
- [Trusted Video Inpainting Localization via Deep Attentive Noise Learning](https://mlsanity.com/paper/2406.13576/) (2024)
- [Spatiality-Frequency Domain Video Forgery Detection System Based on ResNet-LSTM-CBAM and DCT Hybrid Network](https://mlsanity.com/paper/2609.27904/) (2026)

## Abstract (arXiv)

We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy-moves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions.