US 12,081,827 B2
Determining video provenance utilizing deep learning
Alexander Black, London (GB); Van Tu Bui, Guildford (GB); John Collomosse, Woking (GB); Simon Jenni, Hägendorf (CH); and Viswanathan Swaminathan, Saratoga, CA (US)
Assigned to Adobe Inc., San Jose, CA (US); and University of Surrey, Guildford (GB)
Filed by Adobe Inc., San Jose, CA (US); and University of Surrey, Guildford (GB)
Filed on Aug. 26, 2022, as Appl. No. 17/822,573.
Prior Publication US 2024/0073478 A1, Feb. 29, 2024
Int. Cl. H04N 21/434 (2011.01); G06F 16/732 (2019.01); G06F 16/78 (2019.01); H04N 21/84 (2011.01); H04N 21/845 (2011.01)
CPC H04N 21/4341 (2013.01) [G06F 16/732 (2019.01); G06F 16/7867 (2019.01); H04N 21/84 (2013.01); H04N 21/8456 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
sub-dividing a query video into visual segments and audio segments;
generating visual descriptors for the visual segments of the query video utilizing a visual neural network encoder;
generating audio descriptors for the audio segments of the query video utilizing an audio neural network encoder;
determining video segments from a plurality of known videos that are similar to the query video based on the visual descriptors and audio descriptors utilizing an inverse index by:
mapping the visual descriptors and the audio descriptors to codewords; and
identifying the video segments from the plurality of known videos based on the mapped codewords; and
identifying a known video of the plurality of known videos that corresponds to the query video from the determined video segments.