Topological Data Analysis for Image Forgery Detection
DOI:
https://doi.org/10.37506/ijfmt.v14i3.10668Keywords:
Image forgery. Image inpainting. Topological Data Analysis. Local Binary patterns. kNN classifier.Abstract
The manipulation of digital images has become easy due to powerful computers, advanced photo-editing
software packages and high-resolution image-capturing devices. The identification of image authenticity
has received much attention because of the increasing power of image editing methods. Object removal is
an image forgery technique, which is usually achieved by the Exemplar-Based Inpainting (EBI) method
without any noticeable traces. Some legal issues may arise when a tampered image cannot be distinguished
from a real one by visual examination. Therefore the manipulation of digital images has become a huge
challenge to passive image forensics. There are a lot of forgery techniques that use to detect on these images
after removing the object, but these techniques have limitations when used some post-processing operations
such as super-resolution processing, noise addition, blurring and compression processes. To address this
issue, this paper proposes a novel forgery detection technique to recognize tampered inpainting images,
using topological data analysis (TDA) approach. TDA is a mathematical approach concern studying shapes
or objects to gain information about connectivity and closeness property of those objects. This proposed
technique is applied for a large number of natural images with getting a good results.
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