  Camera model identification has earned paramount importance in the field of
image forensics with an upsurge of digitally altered images which are
constantly being shared through websites, media, and social applications. But,
the task of identification becomes quite challenging if metadata are absent
from the image and/or if the image has been post-processed. In this paper, we
present a DenseNet pipeline to solve the problem of identifying the source
camera-model of an image. Our approach is to extract patches of 256*256 from a
labeled image dataset and apply augmentations, i.e., Empirical Mode
Decomposition (EMD). We use this extended dataset to train a Neural Network
with the DenseNet-201 architecture. We concatenate the output features for 3
different sizes (64*64, 128*128, 256*256) and pass them to a secondary network
to make the final prediction. This strategy proves to be very robust for
identifying the source camera model, even when the original image is
post-processed. Our model has been trained and tested on the Forensic
Camera-Model Identification Dataset provided for the IEEE Signal Processing
(SP) Cup 2018. During testing we achieved an overall accuracy of 98.37%, which
is the current state-of-the-art on this dataset using a single model. We used
transfer learning and tested our model on the Dresden Database for Camera Model
Identification, with an overall test accuracy of over 99% for 19 models. In
addition, we demonstrate that the proposed pipeline is suitable for other
image-forensic classification tasks, such as, detecting the type of
post-processing applied to an image with an accuracy of 96.66% -- which
indicates the generality of our approach.
