Research interests

My research focuses on digital image forensics and the use of machine learning to detect image manipulation. My doctoral thesis, Passive Blind Image Forensics Using Machine Learning, Deep Learning, and Quantum Machine Learning Techniques, explored conventional, deep-learning, and quantum-learning approaches to image-splicing detection.

Research contributions

  • Developed a Kekre Transform-based approach that reduced the feature space to less than 1% of image pixels for forged-image classification.
  • Developed MissMarple, a twin-feature transfer-learning convolutional neural network for image-splicing detection.
  • Investigated quantum machine learning for image-forgery detection, including quantum transfer learning for image-splicing classification.
  • Co-authored AbhAS, a realistic image-splicing forensics dataset, and evaluated it using established image-processing transforms and machine-learning classifiers.
  • Conducted a bibliometric analysis of digital image-forensics research using Scopus data and the PRISMA methodology.
  • Studied behavioral responses, privacy considerations, and awareness related to digital image forgery.

My broader interests include deep learning, machine learning, computer vision, cybersecurity, security management, database security, and cyberpsychology. In industry, I continue to apply AI/ML to security analytics and threat-intelligence problems.

Selected publications

  • Gokhale, A., Mulay, P., Pramod, D., & Kulkarni, R. (2020). “A bibliometric analysis of digital image forensics.” Science & Technology Libraries, 39(1), 96–113.
  • Gokhale, A., Pande, M. B., & Pramod, D. (2020). “Implementation of a quantum transfer learning approach to image splicing detection.” International Journal of Quantum Information, 18(05), 2050024.
  • Gokhale, A. L., Thepade, S. D., Aarons, N. R., Pramod, D., & Kulkarni, R. (2022). “AbhAS: A novel realistic image splicing forensics dataset.” Journal of Applied Security Research, 17(1), 80–102.
  • Gokhale, A. L., Pramod, D., Thepade, S. D., & Kulkarni, R. (2021). “MissMarple: A novel socio-inspired feature-transfer learning deep network for image splicing detection.” arXiv preprint arXiv:2112.08018.
  • Gokhale, A., Pradhan, V., Pramod, D., & Kulkarni, R. (2021). “Analyzing the Gender-typed Negative Emotional Strain regarding Digital Image Forgery.” Best Paper Award.

Selected writing

In Before the AI Boom: A Look Back in Time!, I trace ideas about machine intelligence from Ada Lovelace and Alan Turing to modern AI, reflecting on how that history can enrich teaching and discussion.