Histogram Comparison Toolkit for Art Authentication

Added Jun 2025 3 design docs

Art authentication disputes often hinge on subtle material evidence, and pigment usage leaves measurable fingerprints in the color statistics of a photographed work. Galleries, appraisers, and legal teams involved in provenance cases need accessible quantitative tools that complement expert opinion, since laboratory analysis is expensive and not always available in early screening. In this project the intern creates a Streamlit application for forensic image comparison built on OpenCV and NumPy. Users load photographs of two artworks, and the tool computes and plots channel histograms across multiple color spaces, applies histogram equalization to normalize for differing photographic conditions, and produces similarity scores between the works using histogram comparison metrics. Results are organized with pandas into a comparison report, and the interface visualizes where two pieces align or diverge so a non-technical examiner can interpret the evidence. The intern learns how low-level image statistics become decision-support evidence, gains hands-on practice with color space theory, histogram operations, and similarity measures, and demonstrates the judgment needed to present quantitative findings honestly, as supporting signals rather than verdicts, in a domain where conclusions carry legal weight.

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