A fast technique to detect copy-move image forgery with reflection and non-affine transformation attacks

Dixit, Anuja; Bag, Soumen

doi:10.1016/j.eswa.2021.115282

Cited by 20 publications

(7 citation statements)

References 32 publications

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“…This section exists independently in this review because it is now the subject of study within the field of FDE as examiners are called upon more and more to examine digital documents. In the period under review nine articles [ [647] , [648] , [649] , [650] , [651] , [652] , [653] , [654] , [655] ], 13 conference presentations [ [656] , [657] , [658] , [659] , [660] , [661] , [662] , [663] , [664] , [665] , [666] , [667] , [668] ], and five workshops [ [669] , [670] , [671] , [672] , [673] ] were dedicated to this subject.…”

Section: Forensic Document Examinationmentioning

confidence: 99%

“…Dixit [ 650 ] proposed a forgery detection technique in which Center Surround Extrema (CenSurE) detector is applied for key point detection from images. Experimental results show that the proposed technique could effectively detect forged images containing reflection and non-affine transformation with geometrical attacks, as well as robustness against erosion, dilation, RGB color addition, zoom motion blur, JPEG compression, spread noise addition, and multiple copy-move attacks.…”

Section: Forensic Document Examinationmentioning

confidence: 99%

See 1 more Smart Citation

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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Section: Forensic Document Examinationmentioning

confidence: 99%

Section: Forensic Document Examinationmentioning

confidence: 99%

Interpol questioned documents review 2019–2022

Deviterne-Lapeyre

Ibrahim²

2023

Forensic Science International: Synergy

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“…Dixit, Anuja et.al (2021) worked on a technique technique to detect copy-move image forgery with reflection and nonaffine transformation attacks [27]. The detection technique was based on Center Surround Extrema (CenSurE) detector.…”

Section: Brisk>surf>sift>akaze>kaze IImentioning

confidence: 99%

Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries

Singh

2023

Preprint

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With advancement in technology, especially in imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many forgery detection techniques have been developed from time to time. Copy Move Forgery is one such technique which is applied in conjunction with various other techniques to find forged regions within an image. For rapid and accurate detection of forged image, a novel hybrid technique is used in this research work that implements Gray Level Co-Occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoints (BRISK). GLCM significantly extracts key attributes from an image efficiently which will help to increase the detection accuracy. BRISK is known to be one of the 3 fastest modes of detection which will increase the execution speed of GLCM. BRISK even processes scaled and rotated images. Then the Principal Component Analysis (PCA) algorithm is applied in the final phase of detection will remove any unrequited element from the scene and highlights the concerned forged area.

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“…After detecting 68 face landmarks, a set of predefined transformations are applied per each warping functions. To update the location of neighboring face landmarks, five selected landmarks are used to estimate an affine transformation [5] between the reference and input space. This transformation is encoded in a matrix to transform the points from reference to target space and vice versa via a simple matrix multiplication.…”

Section: Automatic Face Warpingmentioning

confidence: 99%

RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries

Kasichainula¹,

Mansourifar²,

Shi³

2022

Preprint

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Recent successful adversarial attacks on face recognition show that, despite the remarkable progress of face recognition models, they are still far behind the human intelligence for perception and recognition. It reveals the vulnerability of deep convolutional neural networks (CNNs) as state-of-the-art building block for face recognition models against adversarial examples, which can cause certain consequences for secure systems. Gradient-based adversarial attacks are widely studied before and proved to be successful against face recognition models. However, finding the optimized perturbation per each face needs to submitting the significant number of queries to the target model. In this paper, we propose recursive adversarial attack on face recognition using automatic face warping which needs extremely limited number of queries to fool the target model. Instead of a random face warping procedure, the warping functions are applied on specific detected regions of face like eyebrows, nose, lips, etc. We evaluate the robustness of proposed method in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but hard-label predictions and confidence scores are provided by the target model.

show abstract

A fast technique to detect copy-move image forgery with reflection and non-affine transformation attacks

Cited by 20 publications

References 32 publications

Interpol questioned documents review 2019–2022

Interpol questioned documents review 2019–2022

Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries

RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries

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