Frequency based Digital Image Forgery Detection Through Optimal Threshold Using SOELTP

Abstract

INTRODUCTION: Image forgery detection is a very challenging task now a day. Latest tools and applications make it easy. Artefact change our thought and perceptions.

OBJECTIVES: A forgery detection system is a need of time to detect image forgery.

METHODS: We proposed a blind image forgery detection technique. Optimal threshold-based Enhanced Local Ternary Pattern (OELTP) technique implemented on smoothed image. Features are extracted in the form of frequency to implement Discrete Wavelet Transform (DWT) on the chrominance component of the image. Support Vector Machine is used for classification.

RESULTS: The accuracy of the forgery detection on the proposed technique is better than some of the previous states of work.

CONCLUSION: Image forgery detection system performance has been improved by better localization of the forgery. Performance of the global threshold improved by using the latest technique, and reducing the operational complexity.