Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers
Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great dem...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020029 |
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| author | S. B. G. Tilak Babu Ch Srinivasa Rao |
| author_facet | S. B. G. Tilak Babu Ch Srinivasa Rao |
| author_sort | S. B. G. Tilak Babu |
| collection | DOAJ |
| description | Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed. |
| format | Article |
| id | doaj-art-54980276e621400394f00fdd5d3eab23 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-54980276e621400394f00fdd5d3eab232025-02-03T08:11:49ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-09-016334736010.26599/BDMA.2022.9020029Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized ClassifiersS. B. G. Tilak Babu0Ch Srinivasa Rao1University College of Engineering, JNTU Kakinada, Kakinada 533003, India.Department of ECE, JNTUK UCE, Vizianagaram 535003, India.Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.https://www.sciopen.com/article/10.26599/BDMA.2022.9020029copy move forgery detectionimage authenticationpassive image forgery detectionblind forgery detection |
| spellingShingle | S. B. G. Tilak Babu Ch Srinivasa Rao Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers Big Data Mining and Analytics copy move forgery detection image authentication passive image forgery detection blind forgery detection |
| title | Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers |
| title_full | Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers |
| title_fullStr | Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers |
| title_full_unstemmed | Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers |
| title_short | Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers |
| title_sort | copy move forgery verification in images using local feature extractors and optimized classifiers |
| topic | copy move forgery detection image authentication passive image forgery detection blind forgery detection |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020029 |
| work_keys_str_mv | AT sbgtilakbabu copymoveforgeryverificationinimagesusinglocalfeatureextractorsandoptimizedclassifiers AT chsrinivasarao copymoveforgeryverificationinimagesusinglocalfeatureextractorsandoptimizedclassifiers |