  Detection of different types of image editing operations carried out on an
image is an important problem in image forensics. It gives the information
about the processing history of an image, and also can expose forgeries present
in an image. There have been few methods proposed to detect different types of
image editing operations in a single framework. However, all the operations
have to be known a priori in the training phase. But, in real-forensics
scenarios it may not be possible to know about the editing operations carried
out on an image. To solve this problem, we propose a novel deep learning-based
method which can differentiate between different types of image editing
operations. The proposed method classifies image patches in a pair-wise fashion
as either similarly or differently processed using a deep siamese neural
network. Once the network learns feature that can discriminate between
different image editing operations, it can differentiate between different
image editing operations not present in the training stage. The experimental
results show the efficacy of the proposed method in detecting/discriminating
different image editing operations.
