Abstract:
In recent years due to advancement in video and image editing tools it has
become increasingly easy to modify the multimedia content. The doctored
videos are very di cult to identify through visual examination as artifacts
left behind by processing steps are subtle and cannot be easily captured
visually. Therefore, the integrity of digital videos can no longer be taken
for granted and these are not readily acceptable as a proof-of-evidence in
court-of-law. Hence, identifying the authenticity of videos has become an
important eld of information security.
In this thesis work, we present a novel approach to detect and temporally
localize video inpainting forgery based on optical
ow consistency. The pro-
posed algorithm comprises of two stages. In the first step, we detect if the
given video is inpainted or authentic and in the second step we perform tem-
poral localization. Towards this, we rst compute the optical
ow between
frames. Further, we analyze the goodness of t of chi-square values obtained
from optical
ow histograms using a Guassian mixture model. A threshold
is then applied to classify between authentic and inpainted videos. In the
next step, we extract Transition Probability Matrices (TPMs) by modelling
the optical
ow as rst order Markov process. SVM based classi cation is
then applied on the obtained TPM features to decide whether a block of
non-overlapping frames is authentic or inpainted thus obtaining temporal
localization.
In order to evaluate the robustness of the proposed algorithm, we perform
the experiments against two popular and e cient inpainting techniques. We
test our algorithm on public datasets like PETS and SULFA. The results
show that the approach is e ective against the inpainting techniques. In
addition, it detects and localizes the inpainted frames in a video with high
accuracy and low false positives.
