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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Eshwar Rao, Manju More
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Detection of colorization based image forgeries using convolutional autoencoder method Panchal, Soumyashree Muralidhar; Hanumanthaiah, Asha Kethaganahalli; Doddasiddavanahalli, Bindushree Channabasavaraju; Eshwar Rao, Manju More; Jayaramu, Ambika Belekere
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1114-1126

Abstract

Recently, it has become difficult to recognize and easier to misuse digital images due to the large number of editing tools available. Detecting forgeries in images is crucial for security and forensic purposes. Therefore, this research implements a deep learning (DL) method of convolutional autoencoder (CAE) which improves colorization-based image forgery detection by leveraging spatial and color information, increasing the detection accuracy. At first, the pre-processed input forgery images are used with the wiener filtering-contrast restricted improved histogram equalization (WE-CLAHE) technique. Hybrid dual-tree complex wavelet trigonometric transform (H‑DTCWT) and VGG-16 are used to extract effective features from the clustered data. Improved horse herd optimization (IHH) is employed to reduce the dimensionality of a feature. At last, the CAE model is implemented to significantly recognize the image forgery. The accuracy of CASIA V1 and GRIP datasets of 99.95% and 99.97%, respectively is achieved. Hence, this implemented method obtains a high forgery detection performance than the existing methods.
Co-Authors Doddasiddavanahalli, Bindushree Channabasavaraju Hanumanthaiah, Asha Kethaganahalli Jayaramu, Ambika Belekere Panchal, Soumyashree Muralidhar