Performance and Robustness Analysis for Some Re-sampling Detection Techniques in Digital Images

Nguyễn, Hiếu Cường; Katzenbeisser, Stefan

doi:10.1007/978-3-642-32205-1_31

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2012

2023

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Cited by 4 publications

(1 citation statement)

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“…In order to evaluate robustness, the aforementioned detection techniques were tested under several simple postprocessing operations, such as Gaussian noise addition or JPEG compression. Recently, some authors [6,7] designed testing frameworks to evaluate and compare the robustness of different resampling detectors. With the aim to assess the security of resampling detection, Kirchner and Boehme [8] designed several targeted attacks against the technique of Popescu and Farid [2].…”

Section: Introductionmentioning

confidence: 99%

Robust Resampling Detection in Digital Images

Nguyễn

Katzenbeisser

2012

Communications and Multimedia Security

Self Cite

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Section: Introductionmentioning

confidence: 99%

Robust Resampling Detection in Digital Images

Nguyễn

Katzenbeisser

2012

Communications and Multimedia Security

Self Cite

View full text Add to dashboard Cite

A Machine Learning Based Technique for Detecting Digital Image Resampling

Nguyễn

2016

Intelligent Information and Database Systems

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Exposing image forgery by detecting traces of feather operation

Zheng

Zhu

et al. 2015

Journal of Visual Languages & Computing

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Powerful digital image editing tools make it very easy to produce a perfect image forgery. The feather operation is necessary when tampering an image by copy–paste operation because it can help the boundary of pasted object to blend smoothly and unobtrusively with its surroundings. We propose a blind technique capable of detecting traces of feather operation to expose image forgeries. We model the feather operation, and the pixels of feather region will present similarity in their gradient phase angle and feather radius. An effectual scheme is designed to estimate each feather region pixel׳s gradient phase angle and feather radius, and the pixel׳s similarity to its neighbor pixels is defined and used to distinguish the feathered pixels from un-feathered pixels. The degree of image credibility is defined, and it is more acceptable to evaluate the reality of one image than just using a decision of YES or NO. Results of experiments on several forgeries demonstrate the effectiveness of the technique

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Performance and Robustness Analysis for Some Re-sampling Detection Techniques in Digital Images

Cited by 4 publications

References 14 publications

Robust Resampling Detection in Digital Images

Robust Resampling Detection in Digital Images

A Machine Learning Based Technique for Detecting Digital Image Resampling

Exposing image forgery by detecting traces of feather operation

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