Copy-Move Forgery Detection Based on Local Gabor Wavelets Patterns

Chou, Chao‐Lung; Lee, Jen-Chun

doi:10.1007/978-3-319-76451-1_5

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The detection performance of the proposed CMFD is compared with that of the state-ofthe-art techniques in literature that used the same CoMoFoD dataset and validation metrics to achieve fair comparison. Table 8 presents a comparison of the proposed approach with other popular approaches, namely, HOG [3], HOGM [39], PCET [40], LGWP [41] and Convolutional Kernel Network [42]. The proposed CMFD based on QPCET descriptors provide superior detection efficiency to previous methods.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the performance of any forgery detection scheme with block matching method fundamentally depends on the invariant features used to extract block features and the method used to find the similar block features. [41] LGWP CDR 0.961 0.973 n/a 0.714 n/a 0.501 n/a FDR n/a n/a n/a n/a Liu, Guan, & Zhao [42] Convolutional Kernel Network CDR 0.827 0.784 0.844 0.726 0.781 0.900 0.751 FDR n/a n/a n/a n/a n/a n/a n/a The overall test results indicate that our proposed approach can obtain effective detection results for CMF of colour images under various challenging conditions. The results for simple CMF reveal that the performance of the proposed method is nearly complete and flawless with a nearly perfect CDR and an FDR that is decreased to nil in all categories.…”

Section: Resultsmentioning

confidence: 99%

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Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

2019

KSII TIIS

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Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis. CMF is performed by copying and pasting a part of an image into other portions of it. Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult. A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks. Secondly, invariant features for each block are extracted using QPCET. Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching. Lastly, a new technique is introduced to reduce the flat region-mediated false matches. Experiments are performed on numerous images selected from the CoMoFoD database. MATLAB 2017b is used to employ the proposed method. Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method. Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression. Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate. The proposed approach may serve as a basis for accurate digital image forensic investigations.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

2019

KSII TIIS

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“…But the method does not work when the copied region is scaled. Chou et al (2018) [22] proposed a block-based copy move forgery detection strategy. The image is split into regions and local wavelet Gabor wavelet patterns are extracted from the regions.…”

Section: Introductionmentioning

confidence: 99%

Technology Innovation: Detection of Counterfeit Region in an Image

Shashikala¹,

Ravikumar²

2022

ECS Trans.

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With availability of advanced photo editing tools today, digital images can be tampered with malicious intention at ease. The image tampering can create various ill effects like false disease diagnosis of medical images, defaming people, hiding evidences, creating false claims etc. Since images are being increasingly used as the most creditable information for inferring various conclusion and judgments in many application scenarios, it is necessary to ensure the authenticity of the image and detect any counterfeit regions in the image. Nowadays tampering can be done in sophisticated manner without leaving any trace and it becomes difficult to detect with naked eye. In this work, we survey the state of art existing works on detection of counterfeit or tampered regions in the images. The survey is done to identify the challenges in existing counterfeit detection techniques.

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An efficient copy move forgery detection using deep learning feature extraction and matching algorithm

Agarwal

Verma

2019

Multimed Tools Appl

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Copy-Move Forgery Detection Based on Local Gabor Wavelets Patterns

Cited by 6 publications

References 9 publications

Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

Technology Innovation: Detection of Counterfeit Region in an Image

An efficient copy move forgery detection using deep learning feature extraction and matching algorithm

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