A passive approach for the detection of splicing forgery in digital images

Kaur, Navneet; Jindal, Neeru; Singh, Kulbir

doi:10.1007/s11042-020-09275-w

Cited by 14 publications

(3 citation statements)

References 42 publications

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“…Video forgery refers to any deliberate modi cation of a digital video for fabrication. Digital video forgery detection is the approach used to assess Watermarking Process [10] Figure 5 Digital Signature Process [11] Figure 6 Passive Techniques Copy Move Forgery Example [42] Figure 10 Explanation of Splicing Forgery [49] Figure 11 Upscale Crop Video Forgery [52] Figure 12 Temporal Video Tampering [17]…”

Section: Discussionmentioning

confidence: 99%

Video Forgery Detection for Surveillance Cameras: A Review

Tayfor,

Rashid,

Qader

et al. 2023

Preprint

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Although video recording is now a standard feature in smartphones and other digital devices, digital records are affordable and straightforward to take and distribute on social media. Furthermore, digital recordings have recently become an essential part of our daily lives, ranging from personal experiences to surveillance footage that may now be utilised as evidence of proof. Thus, it is essential to create detection algorithms for this kind of captured video because the number of high-standard counterfeit videos on social networks and other media is increasing exponentially. Evaluating the integrity of surveillance videos is crucial with the advanced equipment of surveillance systems. As a result, the rapid increase in surveillance cameras has made progress in physical security, including in building homes, stores, governmental buildings, agencies, etc. Moreover, surveillance videos have lately been extensively used as significant evidence in the courts. The evidence from the video can be crucial for a court of justice and investigators to comprehend the incidents as they happened. Unfortunately, due to the widespread of video editing programs, the video forgery/ tampering process has become incredibly simple. Consequently, the courts refuse to accept these videos as evidence because there is some margin of doubt about their authenticity and integrity. Therefore, to avoid such a situation in the future, more efforts are needed in video forensics. For this reason, this paper reviews primarily all the techniques and methods proposed so far to find out forgery in surveillance recordings and how the authenticity of such videos is proven.

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Section: Discussionmentioning

confidence: 99%

Video Forgery Detection for Surveillance Cameras: A Review

Tayfor,

Rashid,

Qader

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The algorithm in [27] is used Markov features for extracting features from two domains: DWT and LBP. Then, features are combined from both domains and fed to SVM for classification.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 digital x-rays forgery classification model using deep learning

El-Latif

Khalifa

2023

IJ-AI

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Nowadays, the internet has become a typical medium for sharing digital images through web applications or social media and there was a rise in concerns about digital image privacy. Image editing software’s have prepared it incredibly simple to make changes to an image's content without leaving any visible evidence for images in general and medical images in particular. In this paper, the COVID-19 digital x-rays forgery classification model utilizing deep learning will be introduced. The proposed system will be able to identify and classify image forgery (copy-move and splicing) manipulation. Alexnet, Resnet50, and Googlenet are used in this model for feature extraction and classification, respectively. Images have been tampered with in three classes (COVID-19, viral pneumonia, and normal). For the classification of (Forgery or no forgery), the model achieves 0.9472 in testing accuracy. For the classification of (Copy-move forgery, splicing forgery, and no forgery), the model achieves 0.8066 in testing accuracy. Moreover, the model achieves 0.796 and 0.8382 for 6 classes and 9 classes problems respectively. Performance indicators like Recall, Precision, and F1 Score supported the achieved results and proved that the proposed system is efficient for detecting the manipulation in images.<div align="center"> </div>

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“…In [23], the focus was on splicing forgery detection since it has been one of the most commonly used techniques for image manipulation. In the proposed scheme, the Markov features in both discrete Wavelet transform (DWT) and local binary pattern (LBP) domains were retrieved and integrated for image splicing detection.…”

Section: Related Workmentioning

confidence: 99%

Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

Gill¹,

Sheikh²,

Rajpar³

et al. 2021

Computers, Materials &Amp; Continua

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Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients' medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural networks (CNNs) have contributed a major breakthrough in this type of detection. There has been much interest from both the clinicians and the AI community in the possibility of widespread usage of artificial neural networks for quick diagnosis using medical data for early COVID-19 patient screening. The purpose of this paper is to detect forgery in COVID-19 medical data by using CNN in the error level analysis (ELA) by verifying the noise pattern in the data. The proposed improved ELA method is evaluated using a type of data splicing forgery and sigmoid and ReLU phenomenon schemes. The proposed method is verified by manipulating COVID-19 data using different types of forgeries and then applying the proposed CNN model to the data to detect the data tampering. The results show that the accuracy of the proposed CNN model on the test COVID-19 data is approximately 92%.

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A passive approach for the detection of splicing forgery in digital images

Cited by 14 publications

References 42 publications

Video Forgery Detection for Surveillance Cameras: A Review

Video Forgery Detection for Surveillance Cameras: A Review

COVID-19 digital x-rays forgery classification model using deep learning

Extended Forgery Detection Framework for COVID-19 Medical Data Using Convolutional Neural Network

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