# Counteracting temporal attacks in Video Copy Detection

arXiv: https://arxiv.org/abs/2501.11171 · HTML: https://mlsanity.com/paper/2501.11171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 19 Jan 2025.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [A Self-Supervised Descriptor for Image Copy Detection](https://mlsanity.com/paper/2202.10261/) (2022)
- [ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning](https://mlsanity.com/paper/1908.07410/) (2019)
- [The 2023 Video Similarity Dataset and Challenge](https://mlsanity.com/paper/2306.09489/) (2023)
- [Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval](https://mlsanity.com/paper/2312.09716/) (2023)
- [VADER: Video Alignment Differencing and Retrieval](https://mlsanity.com/paper/2303.13193/) (2023)
- [3D-CSL: self-supervised 3D context similarity learning for Near-Duplicate Video Retrieval](https://mlsanity.com/paper/2211.05352/) (2022)
- [A Dual-level Detection Method for Video Copy Detection](https://mlsanity.com/paper/2305.12361/) (2023)
- [Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval](https://mlsanity.com/paper/2309.08167/) (2023)
- [Relational Self-supervised Distillation with Compact Descriptors for Image Copy Detection](https://mlsanity.com/paper/2405.17928/) (2024)

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

- [Efficient Logic Gate Networks for Video Copy Detection](https://mlsanity.com/paper/2604.21694/) (2026)

## Abstract (arXiv)

Video Copy Detection (VCD) plays a crucial role in copyright protection and content verification by identifying duplicates and near-duplicates in large-scale video databases. The META AI Challenge on video copy detection provided a benchmark for evaluating state-of-the-art methods, with the Dual-level detection approach emerging as a winning solution. This method integrates Video Editing Detection and Frame Scene Detection to handle adversarial transformations and large datasets efficiently. However, our analysis reveals significant limitations in the VED component, particularly in its ability to handle exact copies. Moreover, Dual-level detection shows vulnerability to temporal attacks. To address it, we propose an improved frame selection strategy based on local maxima of interframe differences, which enhances robustness against adversarial temporal modifications while significantly reducing computational overhead. Our method achieves an increase of 1.4 to 5.8 times in efficiency over the standard 1 FPS approach. Compared to Dual-level detection method, our approach maintains comparable micro-average precision (μAP) while also demonstrating improved robustness against temporal attacks. Given 56% reduced representation size and the inference time of more than 2 times faster, our approach is more suitable to real-world resource restriction.