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University of Zilina, Slovakia

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National Taiwan University of Science and Technology, Taiwan, Province of China

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VSB - Technical University of Ostrava, Czech Republic

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Brno University of Technology, Czech Republic

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Brno University of Technology, Czech Republic

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University of Zilina, Slovakia

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Brno University of Technology, Czech Republic

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Ankara University, Turkey

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Academy of Sciences of the Czech Republic, Czech Republic

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University of Rome "La Sapienza", Italy

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University of Economics in Katowice, Katowice, Poland

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Honeywell International, Czech Republic

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Brno University of Technology, Czech Republic

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Sutcu Imam University, Turkey

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Slovak University of Technology, Slovakia

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VSB - Technical University of Ostrava, Czech Republic

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The University of Edinburgh, United Kingdom

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Tomas Bata University in Zlin, Czech Republic

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Technical University of Cluj Napoca, Romania

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National Research University "MPEI", Russian Federation

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Technical University of Cluj Napoca, Romania

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Warsaw University of Technology, Poland

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Isfahan University of Technology, Iran, Islamic Republic Of

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Brno University of Technology, Czech Republic

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University of Zilina, Slovakia

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Academy of Sciences of the Czech Republic, Czech Republic

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VSB - Technical University of Ostrava, Czech Republic

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Czech Technical University in Prague, Czech Republic

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VSB - Technical University of Ostrava, Czech Republic

Otakar Wilfert
Brno University of Technology, Czech Republic

Jan Zidek
VSB - Technical University of Ostrava, Czech Republic


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Adaptive Histogram Equalization Based Image Forensics Using Statistics of DC DCT Coefficients

Neetu Singh, Abhinav Gupta, Roop Chand Jain

DOI: 10.15598/aeee.v16i1.2647


Abstract

The vulnerability of digital images is growing towards manipulation. This motivated an area of research to deal with digital image forgeries. The certifying origin and content of digital images is an open problem in the multimedia world. One of the ways to find the truth of images is finding the presence of any type of contrast enhancement. In this work, novel and simple machine learning tool is proposed to detect the presence of histogram equalization using statistical parameters of DC Discrete Cosine Transform (DCT) coefficients. The statistical parameters of the Gaussian Mixture Model (GMM) fitted to DC DCT coefficients are used as features for classifying original and histogram equalized images. An SVM classifier has been developed to classify original and histogram equalized image which can detect histogram equalized image with accuracy greater than 95% when false rate is less than 5%.

Keywords


CLAHE; DC DCT coefficients; Gaussian Mixture Model; image forensics.

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