Detection of Region Duplication Forgery in Digital Images Using Wavelets and Log-Polar Mapping

Balakrishnan, Arunkumar

doi:10.1109/iccima.2007.271

Cited by 73 publications

(34 citation statements)

References 2 publications

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“…CMFD methods are either keypointbased methods (e. g. [3], [11], [22]) or block-based methods (e. g. [4]- [7], [9], [10], [12]- [21], [23]- [28]). In both cases, preprocessing of the images is possible.…”

Section: Typical Workflow For Copy-move Forgery Detectionmentioning

confidence: 99%

“…a) Matching: High similarity between two feature descriptors is interpreted as a cue for a duplicated region. For block-based methods, most authors propose the use of lexicographic sorting in identifying similar feature vectors (see e. g. [4]- [7], [9], [10], [12]- [19], [21], [23]- [28]). In lexicographic sorting a matrix of feature vectors is built so that every feature vector becomes a row in the matrix.…”

Section: Typical Workflow For Copy-move Forgery Detectionmentioning

confidence: 99%

See 1 more Smart Citation

An Evaluation of Popular Copy-Move Forgery Detection Approaches

Christlein¹,

Rieß²,

Jordan³

et al. 2012

IEEE Trans.Inform.Forensic Secur.

698

486

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Abstract-A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e. g. , matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and ZERNIKE features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.

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Section: Typical Workflow For Copy-move Forgery Detectionmentioning

confidence: 99%

Section: Typical Workflow For Copy-move Forgery Detectionmentioning

confidence: 99%

An Evaluation of Popular Copy-Move Forgery Detection Approaches

Christlein¹,

Rieß²,

Jordan³

et al. 2012

IEEE Trans.Inform.Forensic Secur.

698

486

View full text Add to dashboard Cite

show abstract

“…Myna et al, [40] proposed a method which uses log polar coordinates and wavelet transforms to detect and also localize copy-move forgery. Application of wavelet transform to input image results in dimensionality reduction and exhaustive search is carried out to identify the similar blocks in the image by mapping them to log-polar coordinates and for similarity criterion phase correlation is used.…”

Section: Copy-move Forgery Detectionmentioning

confidence: 99%

Digital Image Forgeries and Passive Image Authentication Techniques: A Survey

Mushtaq¹,

Mir²

2014

IJAST

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“…The SV vector is sorted lexicographically to detect duplicated region. Myna et al [9] proposed a wavelet transform based approach that detects similar blocks in image by mapping them to log-polar co-ordinates and using phase correlation to find similarity. …”

Section: Review Of Literaturementioning

confidence: 99%

Copy-Move Forgery Detection using Orthogonal Wavelet Transforms

Sarode¹,

Vaswani²

2014

IJCA

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With the help of various image editing tools available, it has become easier to alter an image in such a way that it does not leave behind any clues. Copy -Move forgery is a type of image forgery in which a part of digital image is copied and pasted to another part of same image. Since the copied and pasted image comes from the same image, it becomes difficult to detect the forgery. Generally the intention behind CopyMove forgery is to hide important objects in an image. In this paper, an orthogonal wavelet transform based forgery detection method is proposed. Orthogonal wavelet transform is generated from basic orthogonal transforms. We consider generating Discrete Cosine Transform Wavelet (DCTW) transform and Walsh Wavelet (WW) transform from DCT and Walsh orthogonal transforms. The image is divided into overlapping blocks. On each block, DCTW and WW transforms are applied. From each block discriminative features are extracted from coefficients. These feature vectors are lexicographically sorted and block matching step is applied to find duplicated blocks.

show abstract

Detection of Region Duplication Forgery in Digital Images Using Wavelets and Log-Polar Mapping

Cited by 73 publications

References 2 publications

An Evaluation of Popular Copy-Move Forgery Detection Approaches

An Evaluation of Popular Copy-Move Forgery Detection Approaches

Digital Image Forgeries and Passive Image Authentication Techniques: A Survey

Copy-Move Forgery Detection using Orthogonal Wavelet Transforms

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