  Digital security has been an active area of research interest due to the
rapid adaptation of internet infrastructure, the increasing popularity of
social media, and digital cameras. Due to inherent differences in working
principles to generate an image, different camera brands left behind different
intrinsic processing noises which can be used to identify the camera brand. In
the last decade, many signal processing and deep learning-based methods have
been proposed to identify and isolate this noise from the scene details in an
image to detect the source camera brand. One prominent solution is to utilize a
hierarchical classification system rather than the traditional
single-classifier approach. Different individual networks are used for
brand-level and model-level source camera identification. This approach allows
for better scaling and requires minimal modifications for adding a new camera
brand/model to the solution. However, using different full-fledged networks for
both brand and model-level classification substantially increases memory
consumption and training complexity. Moreover, extracted low-level features
from the different network's initial layers often coincide, resulting in
redundant weights. To mitigate the training and memory complexity, we propose a
classifier-block-level hierarchical system instead of a network-level one for
source camera model classification. Our proposed approach not only results in
significantly fewer parameters but also retains the capability to add a new
camera model with minimal modification. Thorough experimentation on the
publicly available Dresden dataset shows that our proposed approach can achieve
the same level of state-of-the-art performance but requires fewer parameters
compared to a state-of-the-art network-level hierarchical-based system.
