{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T05:51:07Z","timestamp":1777096267362,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before\u2014this fake data could include text, images, sounds, videos, etc. This has brought new challenges that require the faster development of tools and techniques to avoid fraudulent activities at pace and scale. Our focus in this research study is to empirically evaluate the use and effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) and Patch-based Neural Networks in the context of successful identification of real and fake images. We chose the healthcare domain as a potential case study where the fake medical data generation approach could be used to make false insurance claims. For this purpose, we obtained publicly available skin cancer data and used recently introduced stable diffusion approaches\u2014a more effective technique than prior approaches such as Generative Adversarial Network (GAN)\u2014to generate fake skin cancer images. To the best of our knowledge, and based on the literature review, this is one of the few research studies that uses images generated using stable diffusion along with real image data. As part of the exploratory analysis, we analyzed histograms of fake and real images using individual color channels and averaged across training and testing datasets. The histogram analysis demonstrated a clear change by shifting the mean and overall distribution of both real and fake images (more prominent in blue and green) in the training data whereas, in the test data, both means were different from the training data, so it appears to be non-trivial to set a threshold which could give better predictive capability. We also conducted a user study to observe where the naked eye could identify any patterns for classifying real and fake images, and the accuracy of the test data was observed to be 68%. The adoption of deep learning predictive approaches (i.e., patch-based and CNN-based) has demonstrated similar accuracy (~100%) in training and validation subsets of the data, and the same was observed for the test subset with and without StratifiedKFold (k = 3). Our analysis has demonstrated that state-of-the-art exploratory and deep-learning approaches are effective enough to detect images generated from stable diffusion vs. real images.<\/jats:p>","DOI":"10.3390\/computation12090173","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T11:09:53Z","timestamp":1724929793000},"page":"173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Deep Learning Model for Detecting Fake Medical Images to Mitigate Financial Insurance Fraud"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5583-1253","authenticated-orcid":false,"given":"Muhammad Asad","family":"Arshed","sequence":"first","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2606-2405","authenticated-orcid":false,"given":"Shahzad","family":"Mumtaz","sequence":"additional","affiliation":[{"name":"School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2911-6480","authenticated-orcid":false,"given":"\u0218tefan Cristian","family":"Gherghina","sequence":"additional","affiliation":[{"name":"Department of Finance, Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, Romania"}]},{"given":"Neelam","family":"Urooj","sequence":"additional","affiliation":[{"name":"Institute of Business Management and Administrative Sciences (IBM & AS), The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6910-7613","authenticated-orcid":false,"given":"Saeed","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan"},{"name":"Department of Experimental Medical Science, Biomedical Center (BMC), Lund University, 22184 Lund, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1284-234X","authenticated-orcid":false,"given":"Christine","family":"Dewi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia"},{"name":"School of Information Technology, Deakin University, Campus 221 Burwood Hwy, Burwood, VIC 3125, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1056\/NEJMra072149","article-title":"Computed Tomography\u2014An Increasing Source of Radiation Exposure","volume":"357","author":"Brenner","year":"2007","journal-title":"N. 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