Speeding-up SIFT based copy move forgery detection using level set approach

Sudhakar, K.; Sandeep, V. M.; Kulkarni, Shreyas

doi:10.1109/icaecc.2014.7002407

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…It is observed that due to the shifting invariant property, DyWT is more accurate than DWT in CMFD and combination of DyWT with SIFT gives more robust results than the conventional CMFD techniques. In [44], SIFT features are extracted from the image and the Chan-vese technique forces SIFT features to centralise only on crucial regions of the image. This method reported faster in matching due to the drastically reduced number of feature points.…”

Section: Fig 2 Publications Located By Searching For 'Cmfd' On Web Omentioning

confidence: 99%

CMFD: a detailed review of block based and key feature based techniques in image copy‐move forgery detection

Soni

Das

Thounaojam

2018

IET Image Processing

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Section: Fig 2 Publications Located By Searching For 'Cmfd' On Web Omentioning

confidence: 99%

CMFD: a detailed review of block based and key feature based techniques in image copy‐move forgery detection

Soni

Das

Thounaojam

2018

IET Image Processing

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“…Highlights of this approach are ease of implementation and robustness. A SIFT based approach was also used to detect the forged areas in the image using geometric transformations [8] to check if there was any tampering. From the observation made in the current literature, there is a need to improve the SIFT algorithm further to define the features in a more robust manner and it is attempted in this research work to propose an improved feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

Image Forgery Detection with SIFT and RANSAC

Pratapur*¹,

D.C²

2019

IJRTE

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in this paper, simulations were performed using SIFT and RANSAC to highlight the forged regions in the doctored image. SIFT algorithm is modified to consider unit vectors as the features of the blocks. Blocks with similar unit vectors were grouped into cluster. Mean values of the clusters were compared to determine the similarity between clusters. Once the clusters were formed, the image was subjected to RANSAC algorithm to determine the geometric transformation and to highlight the forged region in the doctored image. Two simulations were performed to test the performance of the proposed method. First, doctored image with only scaling and next, image with both scaling and rotation were tested. The simulation results are presented in detail.

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“…The block-based methods [3][4][5][6][7][8][9][10][11][12][13] generally extract image features using invariant moment through overlapping block subdivided in rectangular regions. As an alternative to the CMFD, the keypoint-based methods [14][15][16][17][18][19][20][21][22] extract the features from the whole image. In the third family of techniques, block-based and keypoint-based mehtods are integrated to form the hybrid algorithms [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce the running time and enhance the efficiency, the keypoint-based method is another alternative. In CMFD, a lot of researchers have proposed Scale Invariant Feature Transform (SIFT) [14][15][16][17][18][19][20] and Speeded Up Robust Features (SURF) [21,22]. SIFT and SURF achieves better performance than the DCT, PCET, Zernike moments and PCT in the aspects of processing the geometry transformations effectively.…”

Section: Introductionmentioning

confidence: 99%

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Copy-move Forgery Detection Robust to Various Transformation and Degradation Attacks

2018

KSII TIIS

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Trying to deal with the problem of low robustness of Copy-Move Forgery Detection (CMFD) under various transformation and degradation attacks, a novel CMFD method is proposed in this paper. The main advantages of proposed work include: (1) Discrete Analytical Fourier-Mellin Transform (DAFMT) and Locality Sensitive Hashing (LSH) are combined to extract the block features and detect the potential copy-move pairs; (2) The Euclidian distance is incorporated in the pixel variance to filter out the false potential copy-move pairs in the post-verification step. In addition to extracting the effective features of an image block, the DAMFT has the properties of rotation and scale invariance. Unlike the traditional lexicographic sorting method, LSH is robust to the degradations of Gaussian noise and JEPG compression. Because most of the false copy-move pairs locate closely to each other in the spatial domain or are in the homogeneous regions, the Euclidian distance and pixel variance are employed in the post-verification step. After evaluating the proposed method by the precision-recall-F 1 model quantitatively based on the Image Manipulation Dataset (IMD) and Copy-Move Hard Dataset (CMHD), our method outperforms Emam et al.'s and Li et al.'s works in the recall and F 1 aspects.

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Speeding-up SIFT based copy move forgery detection using level set approach

Cited by 17 publications

References 7 publications

CMFD: a detailed review of block based and key feature based techniques in image copy‐move forgery detection

CMFD: a detailed review of block based and key feature based techniques in image copy‐move forgery detection

Image Forgery Detection with SIFT and RANSAC

Copy-move Forgery Detection Robust to Various Transformation and Degradation Attacks

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