Publication2025
A Two-Stage Framework for Medical Image Tampering Detection and Rectification using MITS-GAN and WGAN
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- 2025 4th Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 5 0 Otcon 2025
- Published
- 1 January 2025
- DOI
- 10.1109/otcon65728.2025.11070643
- Scopus ID
- 105012095491
- Authors
- 6
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3.8 Achieve universal health coverage, including financial risk protection and access to essential medicines
Two-stage framework using MITS-GAN and WGAN detects medical image tampering and rectifies artifacts ensuring integrity for MRI, CT and X-ray diagnosis.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Generative adversarial network application for medical image forensics represents deep learning and cybersecurity research.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Medical image integrity verification supports accurate diagnosis and patient safety.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “A Two-Stage Framework for Medical Image Tampering Detection and Rectification using MITS-GAN and WGAN”. Kortex.