# The Effects of Short Video-Sharing Services on Video Copy Detection

arXiv: https://arxiv.org/abs/2403.18158 · HTML: https://mlsanity.com/paper/2403.18158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [FIVR: Fine-grained Incident Video Retrieval](https://mlsanity.com/paper/1809.04094/) (2018)
- [Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection](https://mlsanity.com/paper/2112.04323/) (2021)
- [TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision](https://mlsanity.com/paper/2211.13090/) (2022)
- [A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection](https://mlsanity.com/paper/2203.02654/) (2022)
- [Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval](https://mlsanity.com/paper/2309.11091/) (2023)
- [A Study on the Characteristics of Douyin Short Videos and Implications for Edge Caching](https://mlsanity.com/paper/1903.12399/) (2019)

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

- [MatchLM2Lite: A Scalable MLLM-to-Lite Framework for Reproduced Content Identification](https://mlsanity.com/paper/2606.14786/) (2026)

## Abstract (arXiv)

The short video-sharing services that allow users to post 10-30 second videos (e.g., YouTube Shorts and TikTok) have attracted a lot of attention in recent years. However, conventional video copy detection (VCD) methods mainly focus on general video-sharing services (e.g., YouTube and Bilibili), and the effects of short video-sharing services on video copy detection are still unclear. Considering that illegally copied videos in short video-sharing services have service-distinctive characteristics, especially in those time lengths, the pros and cons of VCD in those services are required to be analyzed. In this paper, we examine the effects of short video-sharing services on VCD by constructing a dataset that has short video-sharing service characteristics. Our novel dataset is automatically constructed from the publicly available dataset to have reference videos and fixed short-time-length query videos, and such automation procedures assure the reproducibility and data privacy preservation of this paper. From the experimental results focusing on segment-level and video-level situations, we can see that three effects: "Segment-level VCD in short video-sharing services is more difficult than those in general video-sharing services", "Video-level VCD in short video-sharing services is easier than those in general video-sharing services", "The video alignment component mainly suppress the detection performance in short video-sharing services".