VADER: Video Alignment Differencing and Retrieval

Alexander Black, S. Jenni, Tu Bui, Md Mehrab Tanjim, Stefano Petrangeli +3 more
3/1/2023

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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

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Cite this paper

@article{black2023vader,
  title  = {VADER: Video Alignment Differencing and Retrieval},
  author = {Alexander Black and S. Jenni and Tu Bui and Md Mehrab Tanjim and Stefano Petrangeli and Ritwik Sinha and Viswanathan Swaminathan and J. Collomosse},
  year   = {2023},
  eprint = {2303.13193},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2303.13193},
  journal = {ICCV 2023 2023}
}

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