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- W4301431391 abstract "Dissemination of digital content over the Internet and social media sites has put forth major concerns about its authenticity and integrity. Specifically, the manipulation of images into their fake version has become common task. Identifying image forgery has become crucial in order to retain the confidence in numerous digital image processing applications. Our proposed method focuses on extracting multiple image attributes solitary and further using a deep ensemble approach to classify images as tampered or original content. In this paper, discriminative multi-queue classifier is fed with four different aspects of images having diversity in color space, edge details, texture, and moments. To achieve this target, different handcrafted image features were fed to the Convolutional Neural Network (CNN) model. Manipulated images are susceptible to duplication of image areas subjected to discrepancies in terms of underlying edge inconsistencies, change in contrast, or anomalies in texture. Despite the capability of CNN as a generic feature extractor to detect spatial patterns, we additionally extracted image characteristics to analyze images and detect various discrepancies. Trained CNN describes image splicing from a unified dataset from different approach and angles. The collaborative results of ensemble network outperformed single networks, thus improving the effectiveness and reliability of our approach with 92.8% accuracy and other performance parameters." @default.
- W4301431391 created "2022-10-05" @default.
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- W4301431391 date "2022-10-06" @default.
- W4301431391 modified "2023-10-17" @default.
- W4301431391 title "Analysis of Visual Descriptors for Detecting Image Forgery" @default.
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- W4301431391 doi "https://doi.org/10.1007/978-981-19-3575-6_47" @default.
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