Ph.D. @ University of Hyderabad
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University of Hyderabad
Data Scientist
June 18, 2026 – Present
Geometric-transformations-parameters-estimation-from-copy-move-forgery-using-image-blobs-and-keypoin
October 18, 2021 – October 18, 2021
Abstract A copy-move forgery is a passive tampering wherein one or more regions have been copied and pasted within the same image. Often, geometric transformations, including scale, rotation, and rotation+scale are applied to the forged areas to conceal the counterfeits to the copy-move forgery detection methods. Recently, copy-move forgery detection using image blobs have been used to tackle the limitation of the existing detection methods. However, the main limitation of blobs-based copy-move forgery detection methods is the inability to perform the geometric transformation estimation. To tackle the above-mentioned limitation, this article presents a technique that detects copy-move forgery and estimates the geometric transformation parameters between the authentic region and its duplicate using image blobs and scale-rotation invariant keypoints. The proposed algorithm involves the following steps: image blobs are found in the image being analyzed; scale-rotation invariant features a
View ProjectUsing-Illumination-and-LBP-on-COVID-CT-Dataset-COVID-19-
March 27, 2021 – March 27, 2021
Using Illumination, LBP and Machine Learning techniques on COVID-CT-Dataset( COVID-19):
View ProjectGukoresha_MTN_MoMo_API_na_Python_Django
February 9, 2021 – February 9, 2021
Gukoresha_MTN_MoMo_API_na_Python_Django — GitHub repository
View ProjectImage-splicing-detection-technique-based-on-Illumination-Reflectance-model-and-LBP
January 11, 2020 – September 12, 2020
Image Splicing Forgery Detection using Illumination-Reflectance model
View ProjectCopy-move-forgery-detection-Image-Blobs_AKAZE-ORB-BRISK-SURF-SIFT-results-on-MICCF220-MICCF2000
November 17, 2019 – November 17, 2019
Copy-Move forgery detection results on MICC-F220 dataset using Image blobs and features: AKAZE, ORB, BRISK, SURF and SIFT
View ProjectCopy-move-forgery-detection-using-image-blobs-BRISK-features.
November 13, 2018 – July 9, 2020
One of the most frequently used types of digital image forgery is copying one area in the image and pasting it into another area of the same image. This is known as the copy-move forgery. In this paper, we present two efficient techniques for Copy-move forgery detection that use image blobs and key-points to tackle the limits of the existing copy-move forgery detection methods. The first method is based on image blobs and BRISK feature. The second method is based on image blobs and AKAZE feature. The two proposed methods utilize the same pipeline, that is image blobs are found in the image being analyzed, then features are extracted in each blob and the matching process between features from different blobs is performed. The two proposed methods are implemented and evaluated on the copy-move forgery standard datasets MICC-F8multi, MICC-F200, and CoMoFoD. Keywords: AKAZE, BRISK, Blob, CMFD, DoG, LoG
View ProjectCopy-Move-forgery-detection-using-DoG-and-ORB
June 10, 2018 – October 23, 2021
Copy–Move forgery or Cloning is a type of Image tampering where a part of the image is copied and pasted on another part of same image. Copy–move forgery detection technique using DoG (Difference of Gaussian) blob detector, with rotation invariant and resistant to noise feature called ORB (Oriented Fast and Rotated Brief) is poroposed.
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated on image forgery detection, indicating a deep but narrow technical focus. While this shows dedication, the lack of diversity in project types (e.g., NLP, traditional ML, data engineering) might suggest a less broad exposure to typical Data Scientist responsibilities. The single listed experience as 'Data Scientist' at a university, with a future start date, provides limited insight into real-world team collaboration or diverse problem-solving scenarios. The target role is 'Data Scientist', but the projects lean heavily into computer vision/image processing, which is a specialized area within data science. More diverse projects would better align with a general Data Scientist role.
Soft Skills & Operational Fit
The provided data is insufficient to assess soft skills or operational fit. The candidate's project descriptions indicate a focus on technical problem-solving.