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Approaches for Digital Image Forgery Detection
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Scholars' Press Aug 2020, 2020
ISBN 10: 6138940245
ISBN 13: 9786138940241
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Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A novel framework of Hybrid Neural Networks with Decision Tree (HNN-DT) is introduced in this book, which is efficient for easy training and testing of images for proficient classification of forgery images. Preprocessing by Wiener filter is explained, then the feature extraction process by SURF and PCA to extract the relevant features for classification has been discussed. It then moves to find the matching similarity by Manhattan distance to determine the matching between original and forgery images. In chapter six, the modified Gabor filter and Centre Symmetric Local Binary Pattern (CS-LBP) based feature extraction method is developed to detect the copy-move image forgery based on the texture feature of input images. Hybrid Neural Networks with Decision Tree (HNN-DT) is applied to the feature extraction to classify the forgery images. Four new approaches and extensions to detect copy-move forgery attacks using hybrid feature extraction with efficient classification are presented. All four approaches address the authentic and forgery images classification issue in a non-noisy environment, whereas one out of these also addresses the issue of spliced image forgery detection. 180 pp. Englisch. Seller Inventory # 9786138940241
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Approaches for Digital Image Forgery Detection
Print on DemandSeller: moluna, Greven, Germany
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rathore Neeraj KumarDr. Neeraj Rathore, Assistant Prof. of Department of Information Technology of Sri G.S. Institute of Technology & Science, Indore, M.P., India. Ph.D. (2014) & ME (2008)-Thapar University, Punjab, BE(2006)Dr. Neele. Seller Inventory # 406091880
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Rathore:Approaches for Digital Image Fo
ISBN 10: 6138940245
ISBN 13: 9786138940241
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Rathore:Approaches for Digital Image Fo
ISBN 10: 6138940245
ISBN 13: 9786138940241
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Rathore:Approaches for Digital Image Fo
ISBN 10: 6138940245
ISBN 13: 9786138940241
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Approaches for Digital Image Forgery Detection
Published by
Scholars' Press Aug 2020, 2020
ISBN 10: 6138940245
ISBN 13: 9786138940241
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -A novel framework of Hybrid Neural Networks with Decision Tree (HNN-DT) is introduced in this book, which is efficient for easy training and testing of images for proficient classification of forgery images. Preprocessing by Wiener filter is explained, then the feature extraction process by SURF and PCA to extract the relevant features for classification has been discussed. It then moves to find the matching similarity by Manhattan distance to determine the matching between original and forgery images. In chapter six, the modified Gabor filter and Centre Symmetric Local Binary Pattern (CS-LBP) based feature extraction method is developed to detect the copy-move image forgery based on the texture feature of input images. Hybrid Neural Networks with Decision Tree (HNN-DT) is applied to the feature extraction to classify the forgery images. Four new approaches and extensions to detect copy-move forgery attacks using hybrid feature extraction with efficient classification are presented. All four approaches address the authentic and forgery images classification issue in a non-noisy environment, whereas one out of these also addresses the issue of spliced image forgery detection.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 180 pp. Englisch. Seller Inventory # 9786138940241
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Approaches for Digital Image Forgery Detection : Efficient Approaches for Digital Image Forgery Detection
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A novel framework of Hybrid Neural Networks with Decision Tree (HNN-DT) is introduced in this book, which is efficient for easy training and testing of images for proficient classification of forgery images. Preprocessing by Wiener filter is explained, then the feature extraction process by SURF and PCA to extract the relevant features for classification has been discussed. It then moves to find the matching similarity by Manhattan distance to determine the matching between original and forgery images. In chapter six, the modified Gabor filter and Centre Symmetric Local Binary Pattern (CS-LBP) based feature extraction method is developed to detect the copy-move image forgery based on the texture feature of input images. Hybrid Neural Networks with Decision Tree (HNN-DT) is applied to the feature extraction to classify the forgery images. Four new approaches and extensions to detect copy-move forgery attacks using hybrid feature extraction with efficient classification are presented. All four approaches address the authentic and forgery images classification issue in a non-noisy environment, whereas one out of these also addresses the issue of spliced image forgery detection. Seller Inventory # 9786138940241
Quantity: 1 available