Benchmarking Scientific Image Forgery Detectors

• URL: http://arxiv.org/abs/2105.12872v1
• Date: Wed, 26 May 2021 22:58:20 GMT
• Title: Benchmarking Scientific Image Forgery Detectors
• Authors: Jo\~ao P. Cardenuto, Anderson Rocha
• Abstract summary: This paper presents an extendable open-source library that reproduces the most common image forgery operations reported by the research integrity community. We create a large scientific forgery image benchmark (39,423 images) with an enriched ground-truth. In addition, concerned about the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset.
• Score: 18.225190509954874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scientific image integrity area presents a challenging research bottleneck, the lack of available datasets to design and evaluate forensic techniques. Its data sensitivity creates a legal hurdle that prevents one to rely on real tampered cases to build any sort of accessible forensic benchmark. To mitigate this bottleneck, we present an extendable open-source library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. Using this library and realistic scientific images, we create a large scientific forgery image benchmark (39,423 images) with an enriched ground-truth. In addition, concerned about the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. The dataset and source-code will be freely available upon acceptance of the paper.

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