  Adherence to scientific community standards ensures objectivity, clarity,
reproducibility, and helps prevent bias, fabrication, falsification, and
plagiarism. To help scientific integrity officers and journal/publisher
reviewers monitor if researchers stick with these standards, it is important to
have a solid procedure to detect duplication as one of the most frequent types
of manipulation in scientific papers. Images in scientific papers are used to
support the experimental description and the discussion of the findings.
Therefore, in this work we focus on detecting the duplications in images as one
of the most important parts of a scientific paper. We propose a framework that
combines image processing and deep learning methods to classify images in the
articles as duplicated or unduplicated ones. We show that our method leads to a
90% accuracy rate of detecting duplicated images, a ~ 13% improvement in
detection accuracy in comparison to other manipulation detection methods. We
also show how effective the pre-processing steps are by comparing our method to
other state-of-art manipulation detectors which lack these steps.
