From image to sensor: Comparative evaluation of multiple PRNU estimation schemes for identifying sensors from NIR iris images

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 work, we evaluate the applicability of different PRNU estimation schemes in accurately deducing sensor information from NIR iris images. We also analyze the impact of a photometric transformation on the estimation process. Experiments involving 12 sensors and 9511 images convey that the Basic and Enhanced Sensor Pattern Noise (SPN) schemes outperform the Maximum Likelihood and Phase-based SPN methods. Experiments also convey the need to explore alternate methods for performing digital image forensics on NIR iris images.