  Detecting manipulations in digital documents is becoming increasingly
important for information verification purposes. Due to the proliferation of
image editing software, altering key information in documents has become widely
accessible. Nearly all approaches in this domain rely on a procedural approach,
using carefully generated features and a hand-tuned scoring system, rather than
a data-driven and generalizable approach. We frame this issue as a graph
comparison problem using the character bounding boxes, and propose a model that
leverages graph features using OCR (Optical Character Recognition). Our model
relies on a data-driven approach to detect alterations by training a random
forest classifier on the graph-based OCR features. We evaluate our algorithm's
forgery detection performance on dataset constructed from real business
documents with slight forgery imperfections. Our proposed model dramatically
outperforms the most closely-related document manipulation detection model on
this task.
