Twofold Video Hashing with Automatic Synchronization

TL;DR

This paper introduces a novel video hashing approach combining automatic synchronization via dynamic time warping and a new feature extraction method called flow hashing, significantly improving robustness against temporal and spatial attacks.

Contribution

The paper presents a new synchronization method using DTW and a robust feature called flow hashing, along with a fusion mechanism for enhanced video hash robustness.

Findings

Enhanced robustness against temporal desynchronization attacks

Effective fusion of DTW and flow hashing improves accuracy

Demonstrated superior performance on real video datasets

Abstract

Video hashing finds a wide array of applications in content authentication, robust retrieval and anti-piracy search. While much of the existing research has focused on extracting robust and secure content descriptors, a significant open challenge still remains: Most existing video hashing methods are fallible to temporal desynchronization. That is, when the query video results by deleting or inserting some frames from the reference video, most existing methods assume the positions of the deleted (or inserted) frames are either perfectly known or reliably estimated. This assumption may be okay under typical transcoding and frame-rate changes but is highly inappropriate in adversarial scenarios such as anti-piracy video search. For example, an illegal uploader will try to bypass the 'piracy check' mechanism of YouTube/Dailymotion etc by performing a cleverly designed non-uniform resampling of the video. We present a new solution based on dynamic time warping (DTW), which can implement automatic synchronization and can be used together with existing video hashing methods. The second contribution of this paper is to propose a new robust feature extraction method called flow hashing (FH), based on frame averaging and optical flow descriptors. Finally, a fusion mechanism called distance boosting is proposed to combine the information extracted by DTW and FH. Experiments on real video collections show that such a hash extraction and comparison enables unprecedented robustness under both spatial and temporal attacks.