Passive copy-move forgery detection using SIFT, HOG and SURF features

Prasad, Shiv; Ramkumar, B.

doi:10.1109/rteict.2016.7807915

Cited by 14 publications

(10 citation statements)

References 9 publications

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“…With the same position and scale, it produces key points, but distinct directions. In the situation of the image test L(x, y,σ) given to scales , an direction ∅(x,y) and gradients size m(x,y) are pre-calculated by pixels variance using the In the given formulas [18]. In the given formulas:…”

Section: Scale Invariant Feature Transformmentioning

confidence: 99%

Copy Move Forgery Detection Using Forensic Images

Sewan

Altaei

2021

eijs

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Digital images are open to several manipulations and dropped cost of compact cameras and mobile phones due to the robust image editing tools. Image credibility is therefore become doubtful, particularly where photos have power, for instance, news reports and insurance claims in a criminal court. Images forensic methods therefore measure the integrity of image by apply different highly technical methods established in literatures. The present work deals with copy move forgery images of Media Integration and Communication Center Forgery (MICC-F2000) dataset for detecting and revealing the areas that have been tampered portion in the image, the image is sectioned into non overlapping blocks using Simple liner iterative clustering (SLIC) method. Then, Scale invariant feature transform (SIFT) descriptor is applied on the grey of the handled image to gives distinctive key points that classified by K-Nearest neighbor to detect and localize the forged portion in the tempered image. The forgery detection results gave a performance percent of about 98%, which reflects the ability of the KNN classifier that cooperated with SIFT descriptor to detect the forged portions even if the forged area is rotated or scaled or both of them.

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Section: Scale Invariant Feature Transformmentioning

confidence: 99%

Copy Move Forgery Detection Using Forensic Images

Sewan

Altaei

2021

eijs

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“…Shi et al [18] checked two different types of statistical features proposing a natural image model on Columbia Image Splicing Dataset attaining detection accuracy of 91.87%. In [19] presented an algorithm for tampering detection using SVD. A small window of size B x B is slid over the input image to separate the image into overlapping blocks.…”

Section: Related Workmentioning

confidence: 99%

Image tampering detection using genetic algorithm

Agarwal

Pant

2019

MATEC Web Conf.

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As digital images become an indispensable source of information, the authentication of digital images has become crucial. Various techniques of forgery have come into existence, intrusive, and non-intrusive. Image forgery detection hence is becoming more challenging by the day, due to the unwavering advances in image processing. Therefore, image forensics is at the forefront of security applications aiming at restoring trust and acceptance in digital media by exposing counterfeiting methods. The proposed work compares between various feature selection algorithms for the detection of image forgery in tampered images. Several features are extracted from normal and spliced images using spatial grey level dependence method and many more. Support vector machine and Twin SVM has been used for classification. A very difficult problem in classification techniques is to pick features to distinguish between classes. Furthermore, The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. At last, classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features.

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“…Pandey et al [35] designed a method that uses SURF and SIFT robust in detecting copymoved regions. Prasad et al [36] compared the SURF and hybrid features such as SURF-HOG and SIFT-HOG, Histogram of Oriented Gradients (HOG), and the copy-move forgery detection using image features like SIFT, Lee [37] provided a new image searching method extracting features and combine Advanced Speed up Robust Feature (ASURF) and Domain Color Descriptor (DCD). In comparison with open source OpenSURF, their algorithm exhibits a dramatic improvement in retrieval effectiveness.…”

Section: Related Workmentioning

confidence: 99%

Exploring Rotation and Scale Invariant Features in Image Pla- giarism Detection Using Manifold-Ranking Algorithm

Jahangard¹

2018

IJET

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Plagiarism, as a crucial offense especially in academia, not only is well-known problem in text but also is becoming widespread in image. In this work, the performance of manifold-ranking, known as robust method among semi-supervised methods, has been investigated by using twelve different features. As its high performance is attributed to the quality of constructed graph, we applied robust k-regular nearest neighbor (k-RNN) graph in the framework of manifold-ranking based retrieval. Among all tested feature point detectors and descriptors, Root-SIFT, the feature point ones, due to it is invariant to an array of image transforms, is the most reliable feature for calculating image similarity. The database consisting of images from scientific papers containing four popular benchmark test images served to test these methods.

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Passive copy-move forgery detection using SIFT, HOG and SURF features

Cited by 14 publications

References 9 publications

Copy Move Forgery Detection Using Forensic Images

Copy Move Forgery Detection Using Forensic Images

Image tampering detection using genetic algorithm

Exploring Rotation and Scale Invariant Features in Image Pla- giarism Detection Using Manifold-Ranking Algorithm

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