Image Forgery Detection using Deep Learning: A Survey

Barad, Zankhana J.; Goswami, Mukesh M.

doi:10.1109/icaccs48705.2020.9074408

Cited by 51 publications

(15 citation statements)

References 22 publications

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“…Currently, machine learning methods and, in particular, artificial neural networks have been actively developed 16,17 . The use of artificial neural networks makes it possible to find various types of falsifications, including those falsifications that are created by neural networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-step intelligent approach for photo image fragment forgery detection and identification

Stolbova

Malyshev

Golovnin

2023

2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CM

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The paper presents a two-step approach for detection and identification of fake (forged) fragments in photographic images. At the first step, the image is assigned to an original or a fake class by sequentially analyzing the image metadata (EXIF), then performing the error level analysis (ELA), and finally performing neural network analysis of the image. At the second step, the fake image is divided into fragments, each one is analyzed using a neural network for the fake detection, then the fragments that had recognized as a fake are analyzed by a classifying neural network, which makes it possible to identify specific types of fakes. Software has been developed that implements the proposed approach. The training of the used neural networks was carried out on the CASIA V2 dataset, which contains 4795 images (1701 genuine and 3274 fakes). Studies have been carried out to verify the effectiveness, for which, using the developed software 60 photo images were processed, 30 of which are genuine, and 30 are fakes. The developed software correctly identified both the fake fragments and determined their types.

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Section: Literature Reviewmentioning

confidence: 99%

Two-step intelligent approach for photo image fragment forgery detection and identification

Stolbova

Malyshev

Golovnin

2023

2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CM

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“…Passive image forensics [14] is also known as digital image Blind Forensics, where the "blind" means that the forensics can be performed directly from the image without a pre-embedded digital watermark or signature, which is more widely applicable compared to active forensics.…”

Section: Image Passive Forensics Technologymentioning

confidence: 99%

Deep Learning Based Image Forgery Detection Methods

Xiu-jian¹,

Sun²

2022

Journal of Cyber Security

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Increasingly advanced image processing technology has made digital image editing easier and easier. With image processing software at one's fingertips, one can easily alter the content of an image, and the altered image is so realistic that it is illegible to the naked eye. These tampered images have posed a serious threat to personal privacy, social order, and national security. Therefore, detecting and locating tampered areas in images has important practical significance, and has become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely used in image tampering localization, and the achieved performance has significantly surpassed traditional tampering forensics methods. This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning. According to the two types of tampering detection based on deep learning, the detection tasks of the method are detailed separately, and the problems and future prospects in this field are discussed. It is quite different from the existing work: 1) This paper mainly focuses on the problem of image tampering detection, so it does not elaborate on various forensic methods. 2) This paper focuses on the detection method of image tampering based on deep learning. 3) This paper is driven by the needs of tampering targets, so it pays more attention to sorting out methods for different tampering detection tasks.

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“…First, the Harris corner detection technique is applied to an image. In the second step, after extracting the patches, the matching process is done around each patch using convolutional neural networks (CNN) [5,9]. We use a method inspired by Siamese networks [10].…”

Section: Figure 1 An Example Of Copy-move Forgerymentioning

confidence: 99%

Copy-move forgery detection using convolutional neural network and K-mean clustering

Pourkashani

Shahbahrami

Akoushideh

2021

IJECE

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Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms.

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Image Forgery Detection using Deep Learning: A Survey

Cited by 51 publications

References 22 publications

Two-step intelligent approach for photo image fragment forgery detection and identification

Two-step intelligent approach for photo image fragment forgery detection and identification

Deep Learning Based Image Forgery Detection Methods

Copy-move forgery detection using convolutional neural network and K-mean clustering

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