VADER: Video Alignment Differencing and Retrieval

Alexander Black, Simon Jenni, Tu Bui, Md. Mehrab Tanjim, Stefano Petrangeli, Ritwik Sinha, Viswanathan Swaminathan, John Collomosse; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 22357-22367

Abstract

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 https://github.com/AlexBlck/vader

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Black_2023_ICCV, author = {Black, Alexander and Jenni, Simon and Bui, Tu and Tanjim, Md. Mehrab and Petrangeli, Stefano and Sinha, Ritwik and Swaminathan, Viswanathan and Collomosse, John}, title = {VADER: Video Alignment Differencing and Retrieval}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {22357-22367} }