SImProv: Scalable Image Provenance Framework for Robust Content Attribution
This addresses the need for robust content attribution in digital media, offering a scalable solution for verifying image authenticity, though it is incremental in improving existing methods.
The paper tackles the problem of attributing manipulated images to their originals by introducing SImProv, a scalable framework that retrieves top-k similar images, identifies the original, and detects manipulations, achieving effective results on a dataset of 100 million images.
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.