Scale invariant feature transform based copy-move forgery detection techniques on electronic images — A survey

Revi, K. Remya; Wilscy, M.

doi:10.1109/icpcsi.2017.8392130

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Examines both spatial and temporal aspects of images or videos to detect inconsistencies or anomalies. 16 Resource-intensive, especially for video analysis, and may face challenges in real-time applications.…”

Section: Spatio-temporal Analysismentioning

confidence: 99%

Securing Visual Integrity: Machine learning approaches for forged image detection

Patil,

Raut,

Shirsat

et al. 2024

JIST

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Image forgery detection is a critical area of digital forensics, attempting to discover manipulated regions within images to assure their authenticity and integrity. This study investigates the use of machine learning techniques, particularly the Convolutional Neural Networks for image fraud detection. The suggested method involves training classifier to distinguish between original and counterfeit images using extracted features or patches. An image dataset is divided into training and testing sets in this study to facilitate CNN training on patches corresponding to original images. The accuracy of the trained model in identifying phony regions is then evaluated using an additional test set. To measure the effectiveness of the CNN-based forgery detection system, evaluation criteria such as accuracy, precision and recall are used. Proposed system achieves 99.15% accuracy with VGG16 network with tuned parameters.

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Section: Spatio-temporal Analysismentioning

confidence: 99%

Securing Visual Integrity: Machine learning approaches for forged image detection

Patil,

Raut,

Shirsat

et al. 2024

JIST

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“…Many researchers have been interested to copy-move forgery and there exist several methods introduced in this field. CMF is the way of copying a part of an image and pasting it into another region on the same image, while splicing is combining multiple regions of different images to create a forged image [1], [2], [3]. There exist two main categories of techniques used to detect CMF: Block-based and key-point based methods.…”

Section: Introductionmentioning

confidence: 99%

Image forgery detection review

Benhamza

Djeffal

Cheddad

2021

2021 International Conference on Information Systems and Advanced Technologies (ICISAT)

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With the wide spread of digital document use in administrations, fabrication and use of forged documents have become a serious problem. This paper presents a study and classification of the most important works on image and document forgery detection. The classification is based on documents type, forgery type, detection method, validation dataset, evaluation metrics and obtained results. Most of existing forgery detection works are dealing with images and few of them analyze administrative documents and go deeper to analyze their contents.

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Visualizing the truth: a survey of multimedia forensic analysis

Diwan,

Sonkar

2023

Multimed Tools Appl

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Scale invariant feature transform based copy-move forgery detection techniques on electronic images — A survey

Cited by 3 publications

References 11 publications

Securing Visual Integrity: Machine learning approaches for forged image detection

Securing Visual Integrity: Machine learning approaches for forged image detection

Image forgery detection review

Visualizing the truth: a survey of multimedia forensic analysis

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