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S. B. G. Tilak Babu

Bio: S. B. G. Tilak Babu is an academic researcher from Aditya Engineering College. The author has contributed to research in topics: Artificial intelligence & Computer science. The author has an hindex of 2, co-authored 9 publications receiving 13 citations. Previous affiliations of S. B. G. Tilak Babu include Jawaharlal Nehru Technological University, Kakinada.

Papers
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Proceedings ArticleDOI
01 Jul 2020
TL;DR: CMFD method is proposed using Steerable Pyramid Transform, Grey Level Co-occurrence Matrix and Optimized Naive Bayes Classifier and shows robustness over existing algorithms in the literature even the forged image has undergone many attacks.
Abstract: Copy Move Forgery Detection (CMFD) is helpful to detect copied and pasted areas in one image, it plays a crucial role in legal evidence, forensic investigation and in many more places. In this paper, CMFD method is proposed using Steerable Pyramid Transform (SPT), Grey Level Co-occurrence Matrix (GLCM) and Optimized Naive Bayes Classifier (ONBC). The suspected image is given to SPT to obtain different orientations, from all suspected image orientations GLCM features are extracted. These features are used to train ONBC as well as to classify ONBC. Wide range of tests conducted on CoMoFoD, MICC_F and CASIA v1.0 databases using proposed algorithm and performance is measured in terms TPR and FNR. It shows robustness over existing algorithms in the literature even the forged image has undergone many attacks.

50 citations

Journal ArticleDOI
TL;DR: The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation, and the OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).

46 citations

Journal ArticleDOI
TL;DR:
Abstract: Traditionally, nonlinear data processing has been approached via the use of polynomial filters, which are straightforward expansions of many linear methods, or through the use of neural network techniques. In contrast to linear approaches, which often provide algorithms that are simple to apply, nonlinear learning machines such as neural networks demand more computing and are more likely to have nonlinear optimization difficulties, which are more difficult to solve. Kernel methods, a recently developed technology, are strong machine learning approaches that have a less complicated architecture and give a straightforward way to transforming nonlinear optimization issues into convex optimization problems. Typical analytical tasks in kernel-based learning include classification, regression, and clustering, all of which are compromised. For image processing applications, a semisupervised deep learning approach, which is driven by a little amount of labeled data and a large amount of unlabeled data, has shown excellent performance in recent years. For their part, today’s semisupervised learning methods operate on the assumption that both labeled and unlabeled information are distributed in a similar manner, and their performance is mostly impacted by the fact that the two data sets are in a similar state of distribution as well. When there is out-of-class data in unlabeled data, the system’s performance will be adversely affected. When used in real-world applications, the capacity to verify that unlabeled data does not include data that belongs to a different category is difficult to obtain, and this is especially true in the field of synthetic aperture radar image identification (SAR). Using threshold filtering, this work addresses the problem of unlabeled input, including out-of-class data, having a detrimental influence on the performance of the model when it is utilized to train the model in a semisupervised learning environment. When the model is being trained, unlabeled data that does not belong to a category is filtered out by the model using two different sets of data that the model selects in order to optimize its performance. A series of experiments was carried out on the MSTAR data set, and the superiority of our method was shown when it was compared against a large number of current semisupervised classification algorithms of the highest level of sophistication. This was especially true when the unlabeled data had a significant proportion of data that did not fall into any of the categories. The performance of each kernel function is tested independently using two metrics, namely, the false alarm (FA) and the target miss (TM), respectively. These factors are used to calculate the proportion of incorrect judgments made using the techniques.

39 citations

Proceedings ArticleDOI
01 Dec 2016
TL;DR: An efficient method for localization of copy move forgery is proposed in this work for identifying forgery and results are showing robustness even though the forged image has undergone some post processing attacks viz., rotation, flip, JPEG compression.
Abstract: Copy-Move Forgery Detection (CMFD) method is useful for identifying copy and pasted portions in an image. CMFD has demand in forensic investigation, legal evidence and in many other fields. In this paper, the gists of different newly arrived methodologies in current literature are discussed. Some existing methodologies can be able to localize the forged region and some are not. An efficient method for localization of copy move forgery is proposed in this work for identifying forgery. In the proposed methodology, CMFD is achieved by giving suspected image to Steerable Pyramid Transform (SPT), Local Binary Pattern (LBP) is applied on each oriented subband obtained from SPT to extract feature set, then it is used to trained Support Vector Machine (SVM) to classify images into forged or not. Then localization process is carried out on forged images. Results of proposed methodology are showing robustness even though the forged image has undergone some post processing attacks viz., rotation, flip, JPEG compression.

26 citations

Book ChapterDOI
01 Jan 2016
TL;DR: A technique is proposed which uses Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) to identify copy-move forgery and proper selection of similarity and distance thresholds can localize the forged region correctly.
Abstract: Detection of copy move forgery in images is helpful in legal evidence, in forensic investigation and many other fields. Many Copy Move Forgery Detection (CMFD) schemes are existing in the literature. However, most of them fail to withstand post-processing operations viz., JPEG Compression, noise contamination, rotation. Even if able to identify, they consumes much time to detect and locate. In this paper, a technique is proposed which uses Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) to identify copy-move forgery. Features are extracted by using LBP on the LL band obtained by applying DWT on the input image. Proper selection of similarity and distance thresholds can localize the forged region correctly.

24 citations


Cited by
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Proceedings ArticleDOI
01 Jul 2020
TL;DR: CMFD method is proposed using Steerable Pyramid Transform, Grey Level Co-occurrence Matrix and Optimized Naive Bayes Classifier and shows robustness over existing algorithms in the literature even the forged image has undergone many attacks.
Abstract: Copy Move Forgery Detection (CMFD) is helpful to detect copied and pasted areas in one image, it plays a crucial role in legal evidence, forensic investigation and in many more places. In this paper, CMFD method is proposed using Steerable Pyramid Transform (SPT), Grey Level Co-occurrence Matrix (GLCM) and Optimized Naive Bayes Classifier (ONBC). The suspected image is given to SPT to obtain different orientations, from all suspected image orientations GLCM features are extracted. These features are used to train ONBC as well as to classify ONBC. Wide range of tests conducted on CoMoFoD, MICC_F and CASIA v1.0 databases using proposed algorithm and performance is measured in terms TPR and FNR. It shows robustness over existing algorithms in the literature even the forged image has undergone many attacks.

50 citations

Journal ArticleDOI
TL;DR: The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation, and the OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).

46 citations

Proceedings ArticleDOI
01 Dec 2016
TL;DR: An efficient method for localization of copy move forgery is proposed in this work for identifying forgery and results are showing robustness even though the forged image has undergone some post processing attacks viz., rotation, flip, JPEG compression.
Abstract: Copy-Move Forgery Detection (CMFD) method is useful for identifying copy and pasted portions in an image. CMFD has demand in forensic investigation, legal evidence and in many other fields. In this paper, the gists of different newly arrived methodologies in current literature are discussed. Some existing methodologies can be able to localize the forged region and some are not. An efficient method for localization of copy move forgery is proposed in this work for identifying forgery. In the proposed methodology, CMFD is achieved by giving suspected image to Steerable Pyramid Transform (SPT), Local Binary Pattern (LBP) is applied on each oriented subband obtained from SPT to extract feature set, then it is used to trained Support Vector Machine (SVM) to classify images into forged or not. Then localization process is carried out on forged images. Results of proposed methodology are showing robustness even though the forged image has undergone some post processing attacks viz., rotation, flip, JPEG compression.

26 citations

Journal ArticleDOI
TL;DR: In this article , a two-step identification of forgery is presented, in step one, the suspected image will be classified into either one of two classes that are forged or authentic, and step two is carried out only if the suspected is classified as forged, then forged location will be identified using the block-matching procedure.

22 citations