Open AccessForgery localization in images based on joint statistics of image blocks with neighbouring blocks
Author(s) -
Singhal Divya,
Gupta Abhinav
Publication year - 2021
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/ipr2.12160
Subject(s) - jpeg , artificial intelligence , computer science , computer vision , pattern recognition (psychology) , detector , identification (biology) , classifier (uml) , focus (optics) , joint (building) , image (mathematics) , engineering , architectural engineering , botany , physics , optics , biology , telecommunications
Flawless image forensic analysis necessitates precise identification of tampering regions in digital images along with the determination of state of an image (original or forged). Most of the efforts towards localization of forgeries involve localization of information‐changing forgeries with less focus towards localization of information‐preserving forgeries. This paper proposes a new information‐preserving forgery localization method to localize 10 different tampering types exploiting the fact that joint statistics of locally forged regions and neighbouring original regions is disturbed. The proposed 18‐dimensional detector is trained using ensemble classifier for fine‐grain identification of forged regions in post‐JPEG compressed images and results are compared with two recent state‐of‐the‐art detectors. The results demonstrate the effectiveness of the proposed detector with significant performance gain over existing detectors, particularly for localization in images that are compressed with low quality factors of compression.