  In today's age of internet and social media, one can find an enormous volume
of forged images on-line. These images have been used in the past to convey
falsified information and achieve harmful intentions. The spread and the effect
of the social media only makes this problem more severe. While creating forged
images has become easier due to software advancements, there is no automated
algorithm which can reliably detect forgery.
  Image forgery detection can be seen as a subset of image understanding
problem. Human performance is still the gold-standard for these type of
problems when compared to existing state-of-art automated algorithms. We
conduct a subjective evaluation test with the aid of eye-tracker to investigate
into human factors associated with this problem. We compare the performance of
an automated algorithm and humans for forgery detection problem. We also
develop an algorithm which uses the data from the evaluation test to predict
the difficulty-level of an image (the difficulty-level of an image here denotes
how difficult it is for humans to detect forgery in an image. Terms such as
"Easy/difficult image" will be used in the same context). The experimental
results presented in this paper should facilitate development of better
algorithms in the future.
