Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
WWW 2024(2024)
Abstract
In copy-move tampering operations, perpetrators often employ techniques, such
as blurring, to conceal tampering traces, posing significant challenges to the
detection of object-level targets with intact structures. Focus on these
challenges, this paper proposes an Object-level Copy-Move Forgery Image
Detection based on Inconsistency Mining (IMNet). To obtain complete
object-level targets, we customize prototypes for both the source and tampered
regions and dynamically update them. Additionally, we extract inconsistent
regions between coarse similar regions obtained through self-correlation
calculations and regions composed of prototypes. The detected inconsistent
regions are used as supplements to coarse similar regions to refine pixel-level
detection. We operate experiments on three public datasets which validate the
effectiveness and the robustness of the proposed IMNet.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined