Sudipta Banerjee



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Research Interests:

              Sensor forensics for near-infrared iris imagery:

The field of digital image forensics concerns itself with the task of validating the authenticity of an image or determining the device that produced the image. Device or sensor identification can be accomplished by estimating sensor-specific pixel artifacts, such as Photo Response Non Uniformity (PRNU), that leave an imprint in the resulting image. Research in this field has predominantly focused on images obtained using sensors operating in the visible spectrum. Iris recognition systems, on the other hand, utilize sensors operating in the near-infrared (NIR) spectrum. In this scope of research, we focus on sensor identification in the context of near-infrared iris images. Research can be extended to investigate adversarial influences which may deliberately perturb the sensor-specific artifacts and deter sensor identification algorithms. The figure below illustrates a general outline of sensor identification in the context of NIR iris sensor.



      Image Phylogeny Tree (IPT) for digital forensics:

Consider an image that is subjected to a sequence of simple photometric transformations such as gamma correction, histogram equalization, brightness and contrast adjustment, etc. This would result in a family of transformed images. Given a set of such "near-duplicate" images, we develop a method that automatically deduces the relationship between these images and constructs an Image Phylogeny Tree (IPT) that captures their evolutionary structure (i.e., which image originated from which one). We advance the state of the art by (a) first using appropriate basis functions to model the relationship between every image pair, and (b) then using the estimated parameters of the model to depict the relationship between the image pair. It caters to applications pertaining to digital image forensics. The figure below depicts a general outline of IPT construction algorithm


·         S. Banerjee and A. Ross, " Impact of Photometric Transformations on PRNU Estimation Schemes: A Case Study Using Near Infrared Ocular Images," 6th International Workshop on Biometrics and Forensics (IWBF), Sassari, 2018. [To appear]

·         S. Banerjee and A. Ross, "Computing an image Phylogeny Tree from photometrically modified iris images,"IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, 2017, pp. 618-626.doi: 10.1109/BTAS.2017.8272749.URL: .

·         S. Banerjee and A. Ross, "From image to sensor: Comparative evaluation of multiple PRNU estimation schemes for identifying sensors from NIR iris images," 5th International Workshop on Biometrics and Forensics (IWBF), Coventry, 2017, pp. 1-6.doi:10.1109/IWBF.2017.7935081. URL: .

·         V. N. Gangapure, S. Banerjee and A. S. Chowdhury, “Steerable local frequency based multispectral multifocus image fusion”, Information Fusion, Volume 23, 2015 Pages 99-115, ISSN 1566-2535

·         S. Banerjee, V. N. Gangapure and A. S. Chowdhury, “Multispectral Multifocus Image Fusion with Guided Steerable Frequency and Improved Saliency”,  In Proceedings of the Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP '14). ACM, New York, NY, USA, Article 9, 8 pages.