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Median Filtering Forensics based on Convolutional Neural Network and Local Optimal Oriented Patterns
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 38 |
| Year of Publication: 2020 |
| Authors: Ali Ahmad Aminu, Nwojo Nnanna Agwu, Nzurumike Obianuju |
10.5120/ijca2020920957
|
Ali Ahmad Aminu, Nwojo Nnanna Agwu, Nzurumike Obianuju . Median Filtering Forensics based on Convolutional Neural Network and Local Optimal Oriented Patterns. International Journal of Computer Applications. 175, 38 ( Dec 2020), 42-51. DOI=10.5120/ijca2020920957
Abstract
Median filtering forensics has been an active area of research in recent times due to its inherent nature of preserving visual artifacts. To create a convincing image manipulation, Forgers often apply median filtering to destroy statistical traces introduced during image manipulation, hence, median filtering detection has gained wide attention from digital image forensics researchers recently. While many median filtering forensics methods have been developed, the performance of these approaches degrades in low–resolution images compressed with low compression quality factor. This study presents a novel method for median filtering detection based on Local Optimal Oriented Pattern (LOOP) and Convolutional Neural Network (CNN). Here, we employed LOOP, Local textural descriptors of images which can better capture textural variation introduced during image manipulation, as the input of the proposed model. To test the performance of the proposed method, we evaluate its performance using composite datasets formed from five publicly available image datasets. Experimental results demonstrate that the proposed method outperforms some exiting state of the art and could be potentially used to enhance median filtering detection in highly compressed low-resolution images.
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