Copy-Move Image Forgery Detection Using Deep Learning Methods: A Review

Abidin, Arfa Binti Zainal; Majid, Hairudin Bin Abdul; Samah, Azurah Binti A.; Hashim, Haslina

doi:10.1109/icriis48246.2019.9073569

Cited by 22 publications

(8 citation statements)

References 25 publications

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“…Several recent investigations into image forgery detection have been reported [34], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49] of which a few reviewed deep learning (DL)-based techniques [50], [51], [52]. In this survey, we investigate recent trends in image forgery detection approaches for two of the most popular types of image forgery: copy-move images and spliced images.…”

Section: Review Of Recently Published Surveys On Ifd Problemsmentioning

confidence: 99%

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Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Pham

Park

2023

IEEE Access

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In the last decades, deep learning (DL) has emerged as a powerful and dominant technique for solving challenging problems in various fields. Likewise, in the field of digital image forensics, a large and growing body of literature investigates DL-based techniques for detecting and classifying tampered regions in images. This article aims to provides a comprehensive survey of state-of-the-art DL-based methods for image-forgery detection. Copy-move images and spliced images, two of the most popular types of forged images, were considered. Recently, owing to advances in DL, DL-based approaches have yielded much better results as compared to traditional non-DL-based ones. The surveyed techniques were proposed by developing or fusing various efficient DL methods, such as CNN, RCNN, or LSTM to adapt to detecting tampered traces. INDEX TERMSCopy-move image, image forgery, spliced image.

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Section: Review Of Recently Published Surveys On Ifd Problemsmentioning

confidence: 99%

“…The extent to which DL-based methods have been used to solve IFD problems is therefore limited. Abidin et al [52] briefly reviewed DL-based CMFD papers and compared the differences in the detection pipelines of traditional and DL-based methods.…”

Section: Review Of Recently Published Surveys On Ifd Problemsmentioning

confidence: 99%

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Pham

Park

2023

IEEE Access

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“…A technique to detect copy-move forgeries based on SURF and KD-Tree for the comparison of multidimensional data was presented in [11]. Recently, many researchers applied deep learning methods in areas such as mechanics [12], medicine [13], and a solution for copymove detection [14]. [15] proposed a deep learning approach with a transfer learning model that uses VGG-16 custom design convolutional neural network (CNN).…”

Section: Copy-move Detectionmentioning

confidence: 99%

Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Osorio

Robayo

2021

Rev.Fac.Ing.Univ.Antioquia

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Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. For the evaluation of the algorithm, CVLAB, CASIA V1, Columbia, and Columbia Uncompressed datasets were used. Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. The algorithm detected all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal are the reduced number of features needed for identification and its robustness for the file format and image size.

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“…The development of various image editing technologies has eased the image manipulating while making it difficult to distinguish between altered and natural images [4]. Image tampering methods primarily include image retouching, image morphing, resampling, splicing copy move forgery, image generation, and colourisation [4–6]. Individually or a combination of all these techniques are generally used to wrongfully alter the image contents and spread misinformation.…”

Section: Introductionmentioning

confidence: 99%

“…To alleviate illegal operations, various techniques have been developed that encrypt or watermark the data as a line of defence to provide data confidentiality and copyright protection, respectively, [1][2][3].The development of various image editing technologies has eased the image manipulating while making it difficult to distinguish between altered and natural images [4]. Image tampering methods primarily include image retouching, image morphing, resampling, splicing copy move forgery, image generation, and colourisation [4][5][6]. Individually or a combination of all theseThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.…”

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confidence: 99%

Dual branch convolutional neural network for copy move forgery detection

Goel

Kaur

Bala

2020

IET Image Processing

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The advent of digital era has seen a rise in the cases of illegal copying, distribution and forging of images. Even the most secure data channels sometimes suffer to validate the integrity of images. Forgery of multimedia data is devastating in various important applications like defence and satellite. Increased illegal tampering of images has paved way for research in the area of digital forensics. Copy move forgery is one of the various tampering techniques which is used for manipulating an image's content. A deep learning–based passive Copy Move Forgery Detection algorithm is proposed that uses a novel dual branch convolutional neural network to classify images as original and forged. The dual branch convolutional neural network extracts multi‐scale features by employing different kernel sizes in each branch. Fusion of extracted multi‐scale features is then performed to achieve a good accuracy, precision and recall scores. Experiment analysis on MICC F‐2000 dataset has been performed under two different kernel size combinations. Extensive result analysis and comparative analysis proves the efficacy of proposed architecture over existing architecture in terms of performance scores, computation time, and complexity.

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Copy-Move Image Forgery Detection Using Deep Learning Methods: A Review

Cited by 22 publications

References 25 publications

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Dual branch convolutional neural network for copy move forgery detection

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