An evaluation of Error Level Analysis in image forensics

Warif, Nor Bakiah Abd; Idris, Mohd Yamani Idna; Wahab, Ainuddin Wahid Abdul; Salleh, Rosli

doi:10.1109/icsengt.2015.7412439

Cited by 23 publications

(10 citation statements)

References 8 publications

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“…The ELA is capable of identifying manipulation in images even with unchanged regions. The error produced by the ELA technique helps to detect the manipulation in images [27,28].…”

Section: Error Level Analysis (Ela)mentioning

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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“…The ELA is capable of identifying manipulation in images even with unchanged regions. The error produced by the ELA technique helps to detect the manipulation in images [27,28].…”

Section: Error Level Analysis (Ela)mentioning

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

View full text Add to dashboard Cite

show abstract

“…Although these layers are capable of detecting some of these features, they are inadequate to extract all the underlying traces. Thus, we combine EfficientNet [51] with an improved Error Level Analysis (ELA) [56] module for multi-modal feature extraction. Secondly, to solve the problems of local feature dependency, we propose a dense decoder with a novel attention block (GCA) that utilizes multiscale features from the intermediate encoder layers.…”

Section: Tampered Image Original Image Predictionmentioning

confidence: 99%

“…9 shows the output of applying SRM and ELA on a tampered image. ELA has previously been used for localizing compression artefacts from JPEG images [56]. It works by comparing the pixel-wise difference between an image and its compressed copy.…”

Section: A1 Content Suppression Modulesmentioning

confidence: 99%

GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection

Das¹,

Islam²,

Amin³

2021

Preprint

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Forensic analysis depends on the identification of hidden traces from manipulated images. Traditional neural networks fail in this task because of their inability in handling feature attenuation and reliance on the dominant spatial features. In this work we propose a novel Gated Context Attention Network (GCA-Net) that utilizes the non-local attention block for global context learning. Additionally, we utilize a gated attention mechanism in conjunction with a dense decoder network to direct the flow of relevant features during the decoding phase, allowing for precise localization. The proposed attention framework allows the network to focus on relevant regions by filtering the coarse features. Furthermore, by utilizing multi-scale feature fusion and efficient learning strategies, GCA-Net can better handle the scale variation of manipulated regions. We show that our method outperforms state-of-the-art networks by an average of 4.2% − 5.4% AUC on multiple benchmark datasets. Lastly, we also conduct extensive ablation experiments to demonstrate the method's robustness for image forensics.

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“…For example, Gunawan et al [26] apply the ELA for the development of the algorithm for the detection of manipulated images. In another study, Warif et al [27] evaluated the ELA in digital images.…”

Section: Features Of Software Forensic Toolsmentioning

confidence: 99%

Classification and evaluation of digital forensic tools

2020

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Digital forensic tools (DFTs) are used to detect the authenticity of digital images. Different DFTs have been developed to detect the forgery like (i) forensic focused operating system, (ii) computer forensics, (iii) memory forensics, (iv) mobile device forensics, and (v) software forensics tools (SFTs). These tools are dedicated to detect the forged images depending on the type of the applications. Based on our review, we found that in literature of the DFTs less attention is given to the evaluation and analysis of the forensic tools. Among various DFTs, we choose SFTs because it is concerned with the detection of the forged digital images. Therefore, the purpose of this study is to classify the different DFTs and evaluate the software forensic tools (SFTs) based on the different features which are present in the SFTs. In our work, we evaluate the following five SFTs, i.e., "FotoForensics", "JPEGsnoop", "Ghiro", "Forensically", and "Izitru", based on different features so that new research directions can be identified for the development of the SFTs.

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An evaluation of Error Level Analysis in image forensics

Cited by 23 publications

References 8 publications

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

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

GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection

Classification and evaluation of digital forensic tools

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