Publications

VADER: Video Alignment Differencing and Retrieval

International Conference on Computer Vision (ICCV)

Publication date: October 2, 2023

Alex Black, Simon Jenni, Tu Bui, Mehrab Tanjim, Stefano Petrangeli, Ritwik Sinha, Vishy Swaminathan

We propose VADER, a spatio- temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos. VADER matches and coarsely aligns partial video fragments to candidate videos using a robust visual descriptor and scalable search over adaptively chunked video content. A transformer- based alignment module then refines the temporal localization of the query fragment within the matched video. A space- time comparator module identifies regions of manipulation between aligned content, invariant to any changes due to any residual temporal misalignments or artifacts arising from non- editorial changes of the content. Robustly matching video to a trusted source enables conclusions to be drawn on video provenance, enabling informed trust decisions on content encountered. Code and data are available at

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