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The scientific image integrity area presents a challenging researchbottleneck, the lack of available datasets to design and evaluate forensictechniques. Its data sensitivity creates a legal hurdle that prevents one torely on real tampered cases to build any sort of accessible forensic benchmark.To mitigate this bottleneck, we present an extendable open-source library thatreproduces the most common image forgery operations reported by the researchintegrity community: duplication, retouching, and cleaning. Using this libraryand realistic scientific images, we create a large scientific forgery imagebenchmark (39,423 images) with an enriched ground-truth. In addition, concernedabout the high number of retracted papers due to image duplication, this workevaluates the state-of-the-art copy-move detection methods in the proposeddataset, using a new metric that asserts consistent match detection between thesource and the copied region. The dataset and source-code will be freelyavailable upon acceptance of the paper.

Benchmarking Scientific Image Forgery Detectors