A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

Amerini, Irene; Ballan, Lamberto; Caldelli, Roberto; Bimbo, Alberto Del; Serra, Giuseppe

doi:10.1109/tifs.2011.2129512

Cited by 876 publications

(687 citation statements)

References 28 publications

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“…In order to detect a region pasted in multiple regions, we use the g2NN strategy (Amerini et al 2011) to find matching keypoints in keypoints regions. For a given keypoint, the similarity values (inverse cosine distance in the SIFT space) between it and other keypoints are calculated and form a similarity vector.…”

Section: ) Multiple Keypoints Matching In Keypoints Regionmentioning

confidence: 99%

“…Here r T is an empirical value, and according to Amerini et al (2011) it is set to 0.5. If k ( ik  ) is the value where the procedure stops, the corresponding keypoints of 12 , .... k s s s are considered as candidate matching of the given keypoint.…”

Section: ) Multiple Keypoints Matching In Keypoints Regionmentioning

confidence: 99%

“…After detecting of matching keypoints in keypoints region, we employ hierarchical clustering (Amerini et al 2011) and Random Sample Consensus algorithm (RANSAC) (Fischler and Bolles 1981) to filter outliers and remove false alarms. …”

Section: ) Filtering Outliersmentioning

confidence: 99%

“…Keypointbased methods, however, can relatively reliably compensate these shortcomings. In (Amerini et al 2013;Amerini et al 2011;Huang et al 2008) Scale-invariant feature transform (SIFT) and (Bo et al 2010;Jing and Shao 2012) Speed-up robust features (SURF) were used to detect copy-move forgery. Due to the number of keypoints is much less than the number of blocks, computational complexity is greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the number of keypoints is much less than the number of blocks, computational complexity is greatly reduced. In (Amerini et al 2011) the authors also proposed to use g2NN to deal with multiple copies, the hierarchical clustering and Random Sample Consensus (RANSAC) were employed to filter out outliers and significantly improved the detection accuracy. In Kumar et al (2015) a hybrid approach was proposed, where SURF was used to detect the keypoints in the image and Binary Robust Invariant Scalable Keypoints (BRISK) features were used to represent corresponding features at these keypoints.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Zheng

Liu

Ren

et al. 2016

Multidim Syst Sign Process

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Abstract. Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or nonsmooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.

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Section: ) Multiple Keypoints Matching In Keypoints Regionmentioning

confidence: 99%

Section: ) Multiple Keypoints Matching In Keypoints Regionmentioning

confidence: 99%

Section: ) Filtering Outliersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Zheng

Liu

Ren

et al. 2016

Multidim Syst Sign Process

View full text Add to dashboard Cite

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Anti‐Forensics of Multimedia Data and Countermeasures

Ho¹,

Li²

2015

Handbook of Digital Forensics of Multimedia Data and Devices

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Digital Photo Forensics

Krawetz¹

2015

Handbook of Digital Imaging

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The abundance of graphics applications has made it easy for anyone to modify digital pictures. However, modified pictures can cause problems for fields where altered photographs may give the wrong impression. Issues arise when altered images are used to convey misleading news, alter a product's performance in advertisements and scientific research, or are associated with identity theft or court proceedings. Forensic imaging provides algorithms, tools, and techniques for detecting image tampering. This chapter focuses on the problem space and some of the more common approaches to address problems in this field.

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A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

Cited by 876 publications

References 28 publications

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Anti‐Forensics of Multimedia Data and Countermeasures

Digital Photo Forensics

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