  Camera model identification (CMI) has gained significant importance in image
forensics as digitally altered images are becoming increasingly commonplace. In
this paper, a novel convolutional neural network (CNN) architecture is proposed
for CMI with emphasis given on the preprocessing task considered to be
inevitable for removing the scene content that heavily obscures the camera
model fingerprints. Unlike the conventional approaches where fixed filters are
used for preprocessing, the proposed remnant blocks, when coupled with a
classification block and trained end-to-end minimizing the classification loss,
learn to suppress the unnecessary image contents dynamically. This helps the
classification block extract more robust camera model-specific features for CMI
from the remnant of the image. The whole network, called RemNet, consisting of
a preprocessing block and a shallow classification block, when trained on 18
models from the Dresden database, shows 100% accuracy for 16 camera models with
an overall accuracy of 97.59% on test images from unseen devices, outperforming
the state of the art deep CNNs used in CMI. Furthermore, the proposed remnant
blocks, when cascaded with the existing deep CNNs, e.g., ResNet, DenseNet,
boost their performances by a large margin. The proposed approach proves to be
very robust in identifying the source camera models, even if the original
images are post-processed. It also achieves an overall accuracy of 95.11% on
the IEEE Signal Processing Cup 2018 dataset, which indicates its
generalizability.
