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Large Feature Mining With Ensemble Learning for Image Forgery Detection

Large Feature Mining With Ensemble Learning for Image Forgery Detection
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Author(s): Qingzhong Liu (Sam Houston State University, USA) and Tze-Li Hsu (Sam Houston State University, USA)
Copyright: 2022
Pages: 29
Source title: Technologies to Advance Automation in Forensic Science and Criminal Investigation
Source Author(s)/Editor(s): Chung-Hao Chen (Old Dominion University, USA), Wen-Chao Yang (National Central Police University, Taiwan) and Lijia Chen (Henan University, China)
DOI: 10.4018/978-1-7998-8386-9.ch007

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Abstract

The detection of different types of forgery manipulation including seam-carving in JPEG images is a hot spot in image forensics. Seam carving was originally designed for content-aware image resizing. It is also being used for forgery manipulation. It is still very challenging to effectively identify the seam carving forgery under recompression. To address the highly challenging detection problems, this chapter introduces an effective approach with large feature mining. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. The experimental results validate the efficacy of proposed approach to detecting JPEG double compression and exposing the seam-carving forgery while the JPEG recompression is proceeded at the same quality and a lower quality, which is generally much harder for traditional detection methods. The methodology introduced in this chapter provides a strategy and realistic approach to resolve the highly challenging problems in image forensics.

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