{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T01:47:44Z","timestamp":1777945664318,"version":"3.51.4"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition Letters"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.patrec.2026.03.016","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:25:24Z","timestamp":1773825924000},"page":"41-47","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Integrating Fourier analysis and deep learning for robust detection of deep fake brain magnetic resonance images"],"prefix":"10.1016","volume":"204","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7861-1026","authenticated-orcid":false,"given":"Vaishnavi","family":"Ravi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2077-3564","authenticated-orcid":false,"given":"Yogesh K.","family":"Sahu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2745-0303","authenticated-orcid":false,"given":"Prabhas R.","family":"Onteru","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9147-0117","authenticated-orcid":false,"given":"Parag","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4769-3547","authenticated-orcid":false,"given":"Dhanshree","family":"Warokar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6123-497X","authenticated-orcid":false,"given":"Padma","family":"Murali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7465-2750","authenticated-orcid":false,"given":"Rajesh","family":"Katta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6352-6283","authenticated-orcid":false,"given":"Ambedkar","family":"Dukkipati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4810-352X","authenticated-orcid":false,"given":"Phaneendra K.","family":"Yalavarthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.patrec.2026.03.016_bib0001","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"issue":"11","key":"10.1016\/j.patrec.2026.03.016_bib0002","doi-asserted-by":"crossref","first-page":"7327","DOI":"10.1109\/TPAMI.2021.3116668","article-title":"Deep generative modelling: a comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models","volume":"44","author":"Bond-Taylor","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patrec.2026.03.016_bib0003","series-title":"28th USENIX Security Symposium (USENIX Security 19)","first-page":"461","article-title":"{CT-GAN}: malicious tampering of 3D medical imagery using deep learning","author":"Mirsky","year":"2019"},{"key":"10.1016\/j.patrec.2026.03.016_bib0004","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.patrec.2024.12.011","article-title":"MedLesSynth-LD: lesion synthesis using physics-based noise models for robust lesion segmentation in low-data medical imaging regimes","volume":"188","author":"Narayanan","year":"2025","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2026.03.016_bib0005","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1125","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"key":"10.1016\/j.patrec.2026.03.016_bib0006","doi-asserted-by":"crossref","first-page":"25494","DOI":"10.1109\/ACCESS.2022.3154404","article-title":"Deepfake detection: a systematic literature review","volume":"10","author":"Rana","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.patrec.2026.03.016_bib0007","unstructured":"G. Pei, J. Zhang, M. Hu, Z. Zhang, C. Wang, Y. Wu, G. Zhai, J. Yang, C. Shen, D. Tao, Deepfake generation and detection: a benchmark and survey, (2024). arXiv preprint arXiv: 2403.17881."},{"key":"10.1016\/j.patrec.2026.03.016_bib0008","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.patrec.2021.03.032","article-title":"A comparative study on handcrafted features v\/s deep features for open-set fingerprint liveness detection","volume":"147","author":"Agarwal","year":"2021","journal-title":"Pattern Recognit. Lett."},{"issue":"2","key":"10.1016\/j.patrec.2026.03.016_bib0009","article-title":"Deepfake detection method based on spatio-temporal information fusion","volume":"83","author":"Wang","year":"2025","journal-title":"Comput. Mater. Continua"},{"key":"10.1016\/j.patrec.2026.03.016_bib0010","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.patrec.2024.03.025","article-title":"A guided-based approach for deepfake detection: RGB-depth integration via features fusion","volume":"181","author":"Leporoni","year":"2024","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2026.03.016_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111551","article-title":"Anisotropic multiresolution analyses for deepfake detection","volume":"164","author":"Huang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patrec.2026.03.016_bib0012","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.patrec.2024.04.018","article-title":"Generalized and robust model for GAN-generated image detection","volume":"182","author":"Raj","year":"2024","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2026.03.016_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111528","article-title":"Leveraging facial landmarks improves generalization ability for deepfake detection","volume":"164","author":"Gao","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patrec.2026.03.016_bib0014","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.patrec.2024.02.019","article-title":"Deepfake face discrimination based on self-attention mechanism","volume":"183","author":"Wang","year":"2024","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2026.03.016_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109026","article-title":"Image manipulation detection by multiple tampering traces and edge artifact enhancement","volume":"133","author":"Lin","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patrec.2026.03.016_bib0016","article-title":"Machine learning based medical image deepfake detection: a comparative study","volume":"8","author":"Solaiyappan","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"10.1016\/j.patrec.2026.03.016_bib0017","series-title":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"430","article-title":"Identifying obviously artificial medical images produced by a generative adversarial network","author":"O\u2019Reilly","year":"2022"},{"key":"10.1016\/j.patrec.2026.03.016_bib0018","doi-asserted-by":"crossref","first-page":"52205","DOI":"10.1109\/ACCESS.2024.3386644","article-title":"A new approach for effective medical deepfake detection in medical images","volume":"12","author":"Karak\u00f6se","year":"2024","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.patrec.2026.03.016_bib0019","doi-asserted-by":"crossref","first-page":"825","DOI":"10.3390\/diagnostics13050825","article-title":"Leveraging vision attention transformers for detection of artificially synthesized dermoscopic lesion deepfakes using derm-CGAN","volume":"13","author":"Sharafudeen","year":"2023","journal-title":"Diagnostics"},{"issue":"7","key":"10.1016\/j.patrec.2026.03.016_bib0020","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0303332","article-title":"CFDMI-SEC: an optimal model for copy-move forgery detection of medical image using SIFT, EOM and CHM","volume":"19","author":"Amiri","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.patrec.2026.03.016_bib0021","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13550-021-00830-6","article-title":"CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [18 F] FDG PET, T1 and FLAIR MRI, and CT images available for research","volume":"11","author":"M\u00e9rida","year":"2021","journal-title":"EJNMMI Res."},{"key":"10.1016\/j.patrec.2026.03.016_bib0022","unstructured":"S. Bakas, C. Sako, H. Akbari, M. Bilello, A. Sotiras, G. Shukla, J.D. Rudie, N. Flores Santamaria, A. Fathi Kazerooni, S. Pati, et al., Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo glioblastoma (GBM) patients from the university of pennsylvania health system (UPENN-GBM), The Cancer Imaging Archive 10(2021)."},{"key":"10.1016\/j.patrec.2026.03.016_bib0023","unstructured":"L. Scarpace, T. Mikkelsen, S. Cha, S. Rao, S. Tekchandani, D. Gutman, J.H. Saltz, B.J. Erickson, N. Pedano, A.E. Flanders, et al., The cancer genome atlas glioblastoma multiforme collection (TCGA-GBM), The Cancer Imaging Archive(2016)."},{"key":"10.1016\/j.patrec.2026.03.016_bib0024","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10684","article-title":"High-resolution image synthesis with latent diffusion models","author":"Rombach","year":"2022"},{"key":"10.1016\/j.patrec.2026.03.016_bib0025","series-title":"2015 5th IEEE International Conference on System Engineering and Technology (ICSET)","first-page":"23","article-title":"An evaluation of error level analysis in image forensics","author":"Abd Warif","year":"2015"},{"key":"10.1016\/j.patrec.2026.03.016_bib0026","unstructured":"K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, (2014). arXiv preprint arXiv: 1409.1556."},{"issue":"4","key":"10.1016\/j.patrec.2026.03.016_bib0027","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Appl."},{"key":"10.1016\/j.patrec.2026.03.016_bib0028","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4510","article-title":"MobileNetv2: inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"},{"issue":"1","key":"10.1016\/j.patrec.2026.03.016_bib0029","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"issue":"7","key":"10.1016\/j.patrec.2026.03.016_bib0030","doi-asserted-by":"crossref","first-page":"102","DOI":"10.3390\/jimaging7070102","article-title":"Exposing manipulated photos and videos in digital forensics analysis","volume":"7","author":"Ferreira","year":"2021","journal-title":"J. Imaging"},{"issue":"5","key":"10.1016\/j.patrec.2026.03.016_bib0031","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: full training or fine tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patrec.2026.03.016_bib0032","series-title":"International Conference on Machine Learning","first-page":"647","article-title":"Decaf: a deep convolutional activation feature for generic visual recognition","author":"Donahue","year":"2014"},{"key":"10.1016\/j.patrec.2026.03.016_bib0033","first-page":"3347","article-title":"Transfusion: understanding transfer learning for medical imaging","volume":"32","author":"Raghu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.patrec.2026.03.016_bib0034","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2020190043","article-title":"On the interpretability of artificial intelligence in radiology: challenges and opportunities","volume":"2","author":"Reyes","year":"2020","journal-title":"Radiol. Artif. Intell."}],"container-title":["Pattern Recognition Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865526001066?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865526001066?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T12:39:47Z","timestamp":1777725587000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167865526001066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":34,"alternative-id":["S0167865526001066"],"URL":"https:\/\/doi.org\/10.1016\/j.patrec.2026.03.016","relation":{},"ISSN":["0167-8655"],"issn-type":[{"value":"0167-8655","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Integrating Fourier analysis and deep learning for robust detection of deep fake brain magnetic resonance images","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patrec.2026.03.016","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}