Douglas-Quaid -- Open Source Image Matching Library
This addresses the problem of reducing the burden on security teams through partial automation of screenshot classification, though it is incremental as it builds on existing methods.
The paper tackles the lack of open-source, turnkey libraries for image matching in security analysis by introducing Douglas-Quaid, a modular library that provides quality and speed results for visual correlation and image matching tasks.
Security analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks. However, no open-source and turnkey library was found able to reach this goal. The present paper introduces an Open-Source modular library for the specific cases of visual correlation and Image Matching named Douglas-Quaid. The design of the library, chosen tradeoffs, encountered challenges, envisioned solutions as well as quality and speed results are presented in this paper. We also explore researches directions and future potential developments of the library. Our claim is that even partial automation of screenshots classification would reduce the burden on security teams and that Douglas-Quaid is a step forward in this direction.