Twofold Video Hashing with Automatic Synchronization
Mu Li, Vishal Monga

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…
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Taxonomy
TopicsVideo Analysis and Summarization · Advanced Image and Video Retrieval Techniques · Music and Audio Processing
