Counteracting temporal attacks in Video Copy Detection

TL;DR

This paper improves video copy detection by enhancing robustness against temporal attacks and reducing computational costs, making it more practical for real-world applications.

Contribution

It introduces a novel frame selection strategy based on local maxima of interframe differences, improving robustness and efficiency over existing methods.

Findings

Achieves 1.4 to 5.8 times efficiency increase over 1 FPS baseline.

Maintains comparable micro-average precision ($$AP) with improved robustness.

Reduces representation size by 56% and doubles inference speed.

Abstract

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 ($\mu$AP) 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.