SImProv: Scalable Image Provenance Framework for Robust Content
Attribution
- URL: http://arxiv.org/abs/2206.14245v2
- Date: Mon, 8 May 2023 12:05:05 GMT
- Title: SImProv: Scalable Image Provenance Framework for Robust Content
Attribution
- Authors: Alexander Black, Tu Bui, Simon Jenni, Zhifei Zhang, Viswanathan
Swaminanthan, John Collomosse
- Abstract summary: We present SImProv, a framework to match a query image back to a trusted database of originals.
SimProv consists of three stages: a scalable search stage for retrieving top-k most similar images; a re-ranking and near-duplicated detection stage for identifying the original among the candidates.
We demonstrate effective retrieval and manipulation detection over a dataset of 100 million images.
- Score: 80.25476792081403
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present SImProv - a scalable image provenance framework to match a query
image back to a trusted database of originals and identify possible
manipulations on the query. SImProv consists of three stages: a scalable search
stage for retrieving top-k most similar images; a re-ranking and
near-duplicated detection stage for identifying the original among the
candidates; and finally a manipulation detection and visualization stage for
localizing regions within the query that may have been manipulated to differ
from the original. SImProv is robust to benign image transformations that
commonly occur during online redistribution, such as artifacts due to noise and
recompression degradation, as well as out-of-place transformations due to image
padding, warping, and changes in size and shape. Robustness towards
out-of-place transformations is achieved via the end-to-end training of a
differentiable warping module within the comparator architecture. We
demonstrate effective retrieval and manipulation detection over a dataset of
100 million images.
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