Pixel Based Image Forensic Technique for Copy-move Forgery Detection Using Auto Color Correlogram

Malviya, Ashwini V.; Ladhake, S. A.

doi:10.1016/j.procs.2016.03.050

Cited by 43 publications

(12 citation statements)

References 4 publications

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“…The proposed work includes noise filtering, transformation, auto color correlogram for extraction feature, matching forgery detection similarities, Malviya et al [36] (2016). The result shows that accuracy is only 95.65 % with a false positive rate of 16 and a false negative rate of 32 by testing 400 images from the database [37].…”

Section: International Journal Of Engineering and Advanced Technologymentioning

confidence: 99%

Forensic Science Research Summary for Forgery Detection of Digital Images

Monika¹,

Ece²,

Bansal³

et al. 2020

IJEAT

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An important measure of proof collection, storage, and authentication in forensic sciences, which decide the safety and security of any system documents, which can be either portable document formats or scanned images. To gather evidence, or plan a forensic investigation digital images are secured with different modern methodologies. Digital image analysis includes image recovery and surveillance for image information improvement. The goal of forgery detection is to maximize the extraction of information from manipulated images, particularly noisy and post-processed images. Because digital image processing is becoming popular with many advantages in scientific and engineering applications, the forgery techniques are also growing at a rapid rate. Therefore, the main focus is on different types of forgery detection in digital image processing with the help of all transform techniques and comparing their best results for further improvement in order to generate a new approach for a future forensic science investigation.

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Section: International Journal Of Engineering and Advanced Technologymentioning

confidence: 99%

Forensic Science Research Summary for Forgery Detection of Digital Images

Monika¹,

Ece²,

Bansal³

et al. 2020

IJEAT

View full text Add to dashboard Cite

show abstract

“…Examples of some selected recent research utilizing Euclidean distance during their feature matching stage were proposed in [61], [9], [60], [37], [45]. Instead of Euclidean distance, Malviya and Ladhake, in [66] and [78], utilized Manhattan distance [79] as an alternative to Euclidean distance to determine the relationship between pairs of matching features. Hosny et al [9] utilizes Euclidean distance with correlation technique to compare between a pair of PCET moments-based feature descriptors.…”

Section: B Similarity Measuring Techniquesmentioning

confidence: 99%

Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis

Teerakanok

Uehara

2019

IEEE Access

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This paper presents the state-of-the-art technical reviews and analysis of recent copy-move forgery detection (CMFD) techniques. A new CMFD process pipeline was introduced. In addition, the techniques used in each stage of the CMFD pipeline are summarized and classified into small categories. Furthermore, the tables of comparison are provided as a quick reference. This technical review paper is expected to help provide useful insights and updated information regarding recent advancements in CMFD to researchers in the field.

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“…They divided an image into blocks, extracted the color moment of each block and then clustered them into some classes. Malviya et al [39] exploited a CBIR feature extraction scheme to detect a typical forgery by employing Auto Correlogram their work has significant accuracy to detect the forged region. They focus on color content in forges image and extract features and then analyze the color moments and HSV color space of the tampered image [40] .Su et al [41] made a comparison between RGB and HSV color space for image retrieval.…”

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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Pixel Based Image Forensic Technique for Copy-move Forgery Detection Using Auto Color Correlogram

Cited by 43 publications

References 4 publications

Forensic Science Research Summary for Forgery Detection of Digital Images

Forensic Science Research Summary for Forgery Detection of Digital Images

Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis

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

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