Publication: Effective Deep Features for Image Splicing Detection
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Date
2021
Authors
Ahmed I.T.
Hammad B.T.
Jamil N.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
In the last few years, Digital image forgery (DIF) detection has become a prominent subject. Image splicing is a frequent approach for making digital image forgeries. Image splicing creates forged images that are hard to detect immediately. The detection accuracy of most existing image splicing detection algorithms is low, thus there is room for improvement. Therefore, this research provides an image splicing detection (ISD) method based on deep learning. The proposed image splicing detection has three stages: (1) RGB image conversion and image size fitting are examples of image pre-processing. (2) Using the pre-Trained CNN AlexNet model, we extract the final discriminative feature for a preprocessed image. (3) Finally, the generated feature representation is used to train a Canonical Correlation Analysis (CCA) classifier for binary classification (authentic/forged). The accuracy of the proposed approach using a pre-Trained AlexNet model based deep features with CCA classifier is equal to 98.79 % when evaluated on the CASIA v1.0 splicing image forgery database. In comparison, the proposed surpassed existing methods. In the future, the proposed could be applied to other types of image forgery, such as image retouching. � 2021 IEEE.
Description
Correlation methods; Deep learning; Image classification; Image enhancement; Alexnet model; Canonical correlation analyse classifier; Canonical correlations analysis; Deep feature; Digital image; Digital image forgery; Image forgery; Image forgery detections; Image splicing; Image splicing detection; Classification (of information)