Open AccessCNN based localisation of forged region in object‐based forgery for HD videos
Author(s) -
Kohli Aditi,
Gupta Abhinav,
Singhal Divya
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0397
Subject(s) - computer science , artificial intelligence , convolutional neural network , frame (networking) , computer vision , object (grammar) , object detection , pattern recognition (psychology) , telecommunications
The location of the smallest object in a scene plays an essential role in the perception of a viewer. Any tampering with it, may evolve in adverse consequences especially with surveillance videos of banks, ATMs, traffic monitoring etc. Therefore, a scientific approach is required to thoroughly observe the fine details of tampering (forgery) in a video. A spatio‐temporal detection method is proposed using convolutional neural network (CNN) to detect as well as localise the forged region in a forged video frame. The proposed method is employed in two stages. The first stage is detecting forged frames using proposed temporal CNN, while the second stage is localising the forged region in a novel way using proposed spatial CNN. The vital element of a video, i.e. motion residual is used to train the proposed network. Thus, making the network comprehensive in detecting the object‐based forgery in HD videos. The performance of the proposed method is evaluated on SYSU‐OBJFORG dataset (object‐based video forgery dataset) and a derived test dataset of variable length and frame size videos. The results are compared with state‐of‐the‐art methods to prove the efficacy of the proposed method.