Efficient resampling features and convolution neural network model for image forgery detection
The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image fo...
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| Main Authors: | , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2022-01-01.
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| LEADER | 03090 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | 0 nhttps:__ijeecs.iaescore.com_index.php_IJEECS_article_downloadSuppFile_24074_3492 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a S, Manjunatha |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Patil, Malini M. |e author |
| 245 | 0 | 0 | |a Efficient resampling features and convolution neural network model for image forgery detection |
| 260 | |b Institute of Advanced Engineering and Science, |c 2022-01-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24074 | ||
| 520 | |a The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Digital Signal; Image; video; processing | ||
| 690 | |a Convolution neural network; Deep learning; Hybrid attack; Image tampering detection; Resampling feature; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 25, No 1: January 2022; 183-190 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v25.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24074/15874 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/downloadSuppFile/24074/3492 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24074/15874 |z Get fulltext |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/downloadSuppFile/24074/3492 |z Get fulltext |