Detection and Localization of Copy-Move Tampering Using Features of Locality Preserving Projection

Mall, Vinod; Roy, Anil K.; Mitra, Suman K.

doi:10.1109/iciip47207.2019.8985823

Cited by 10 publications

(10 citation statements)

References 17 publications

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“…New methods focus on reducing time complexity while maintaining high performance. Diwan et al (2019) proposed a block-based method using Local Linear Projection (LLP) which has a similarity preserving property. Similar blocks are projected close to each other in the LLP space which allows not to lexicographically sort all the blocks in the image.…”

Section: Recent Copy-move Forgery Detection Methodsmentioning

confidence: 99%

“…In this part we compare our method with previous ones on the entire FAU database. To do this, we compare our method with the previous methods of (Huang et al, 2008;Amerini et al, 2011;Shivakumar and Baboo, 2011;Li et al, 2015;Pun et al, 2015;Zandi et al, 2016;Li and Zhou, 2019;Mei et al, 2019;Chen et al, 2020;Diwan et al, 2019) and (Lyu et al, 2021).…”

Section: Comparison With the Entire Fau Databasementioning

confidence: 99%

“…The scores obtained are better than those of the methods (Huang et al, 2008;Shivakumar and Baboo, 2011;Li et al, 2015;Zandi et al, 2016;Lyu et al, 2021) with F1-scores between 0.6354 and 0.8607, and is comparable to the methods of (Amerini et al, 2011;Pun et al, 2015;Li and Zhou, 2019) and (Mei et al, 2019) which have respectively an F1-score of 0.8780, 0.8997, 0.8838, and 0.9054. Only the (Diwan et al, 2019) and (Chen et al, 2020) methods perform better and have respectively an F1-score of 0.9924 and 0.9976. The first approach is a block-based method that uses the LLP (Local Linear Projection) while the second is a keypoint-based method which starts by extracting SIFT points before classifying them by scale and colour.…”

Section: Comparison With the Entire Fau Databasementioning

confidence: 99%

“…In this part we compare our method with the method of (Bravo-Solorio and Nandi, 2009;Amerini et al, 2011;Cozzolino et al, 2015a;Li et al, 2015;Silva et al, 2015;Zandi et al, 2016;Diwan et al, 2019;Li and Zhou, 2019;Meena and Tyagi, 2020;Tahaoglu et al, 2021;Chen et al, 2020) and (Gupta and Singh, 2021) on the entire GRIP database.…”

Section: Comparison On the Entire Grip Databasementioning

confidence: 99%

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A Very Fast Copy-Move Forgery Detection Method for 4K Ultra HD Images

Bertojo

Néraud

Puech

2022

Front. Signal Process.

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Copy-move forgery detection is a challenging task in digital image forensics. Keypoint-based detection methods have proven to be very efficient to detect copied-moved forged areas in images. Although these methods are effective, the keypoint matching phase has a high complexity, which takes a long time to detect forgeries, especially for very large images such as 4K Ultra HD images. In this paper, we propose a new keypoint-based method with a new fast feature matching algorithm, based on the generalized two nearest-neighbor (g2NN) algorithm allowing us to greatly reduce the complexity and thus the computation time. First, we extract keypoints from the input image. After ordering them, we perform a match search restricted to a window around the current keypoint. To detect the keypoints, we propose not to use a threshold, which allows low intensity keypoint matching and a very efficient detection of copy-move forgery, even in very uniform or weakly textured areas. Then, we apply a new matching algorithm, and finally we compute the cluster thanks to the DBSCAN algorithm. Our experimental results show that the method we propose can detect copied-moved areas in forged images very accurately and with a very short computation time which allows for the fast detection of forgeries on 4K images.

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Section: Recent Copy-move Forgery Detection Methodsmentioning

confidence: 99%

Section: Comparison With the Entire Fau Databasementioning

confidence: 99%

Section: Comparison With the Entire Fau Databasementioning

confidence: 99%

Section: Comparison On the Entire Grip Databasementioning

confidence: 99%

See 2 more Smart Citations

A Very Fast Copy-Move Forgery Detection Method for 4K Ultra HD Images

Bertojo

Néraud

Puech

2022

Front. Signal Process.

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“…In this approach, they are not detecting forgery in the presence of geometrical transforms. A.diwan et al [9] have used a feature of LPP for localising the copy-move area of the image. They have accurately detected and localised forgery in the presence of JPEG, AWGN.…”

Section: Related Workmentioning

confidence: 99%

Keypoint based comprehensive copy‐move forgery detection

Sharma

Roy

Mitra

2020

IET Image Processing

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Verifying the authenticity of a digital image has been challenging problem. The simplest of the image tampering tricks is the copy-move forgery. In copy-move forgery copied portion of the image is pasted on another part of the same image. Geometrical transformations are used on the copied portions of the image before pasting it for the tampered image to look realistic and visually convincing. To make it more complex, other processing approaches may also be applied in the forged region for hiding traces of forgery. These processings are the scale, rotation, JPEG compression, and AWGN. In this paper, an approach based on features of the CenSurE keypoint detector and FREAK descriptor is proposed. This combination has novelty in itself as it has never been used for this purpose before to the best of authors' literature studies. CenSurE detectors are fast and give stable and accurate output even in the case of rotated images, which we club with binary descriptor FREAK. Hierarchical clustering and Neighbourhood search is applied in such a way that it can locate and detect multiple copy-move forgeries. The authors are hopeful that the proposed approach may be used in real-time image authentication and copy-move forgery detection.

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