Medical image tampering detection: A new dataset and baseline Conference Paper
| Authors: | Reichman, B.; Jing, L.; Akin, O.; Tian, Y. |
| Title: | Medical image tampering detection: A new dataset and baseline |
| Conference Title: | 25th International Conference on Pattern Recognition (ICPR2020) |
| Abstract: | The recent advances in algorithmic photo-editing and the vulnerability of hospitals to cyberattacks raises the concern about the tampering of medical images. This paper introduces a new large scale dataset of tampered Computed Tomography (CT) scans generated by different methods, LuNoTim-CT dataset, which can serve as the most comprehensive testbed for comparative studies of data security in healthcare. We further propose a deep learning-based framework, ConnectionNet, to automatically detect if a medical image is tampered. The proposed ConnectionNet is able to handle small tampered regions and achieves promising results and can be used as the baseline for studies of medical image tampering detection. © 2021, Springer Nature Switzerland AG. |
| Keywords: | computerized tomography; medical imaging; pattern recognition; comparative studies; ct scans; computed tomography scan; deep learning; large dataset; healthcare data security; tamper detection; security of data; cyber-attacks; image tampering; large-scale dataset; photoediting |
| Journal Title | Lecture Notes in Computer Science |
| Volume: | 12661 |
| Conference Dates: | 2021 Jan 10-15 |
| Conference Location: | Virtual |
| ISBN: | 0302-9743 |
| Publisher: | Springer |
| Date Published: | 2021-01-01 |
| Start Page: | 266 |
| End Page: | 277 |
| Language: | English |
| DOI: | 10.1007/978-3-030-68763-2_20 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Conference Paper -- Export Date: 3 May 2021 -- Source: Scopus |
