Evaluation of copy-move forgery detection: datasets and evaluation metrics

Al-Qershi, Osamah M.; Khoo, Bee Ee

doi:10.1007/s11042-018-6201-4

Cited by 39 publications

(10 citation statements)

References 52 publications

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“…The evaluation metrics play a vital role in estimating the tampering in images. There are two types of metrics used for the evaluation, pixel-based and image-based [36]. In the pixel-based method, the classification of the pixels is done as copy-move and authentic, whereas in the image-based method, the classification of image is done as either tampered or authentic.…”

Section: Related Workmentioning

confidence: 99%

Cloud‐Based Fusion of Residual Exploitation‐Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Doegar

Hiriyannaiah

Matt³

et al. 2021

BioMed Research International

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Cloud computing has evolved in various application areas such as medical imaging and bioinformatics. It raises the issues of privacy and tampering in the images especially related to the medical field and bioinformatics for various reasons. The digital images are quite vulnerable to be tampered by the interceptors. The credibility of individuals can transform through falsified information in the images. Image tampering detection is an approach to identifying and finding the tampered components in the image. For the efficient detection of image tampering, the sufficient number of features are required which can be achieved by a deep learning architecture-based models without manual feature extraction of functions. In this research work, we have presented and implemented a cloud-based residual exploitation-based deep learning architectures to detect whether or not an image is being tampered. The proposed approach is implemented on the publicly available benchmark MICC-F220 dataset with the k -fold cross-validation approach to avoid the overfitting problem and to evaluate the performance metrics.

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Section: Related Workmentioning

confidence: 99%

Cloud‐Based Fusion of Residual Exploitation‐Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Doegar

Hiriyannaiah

Matt³

et al. 2021

BioMed Research International

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“…Individuals can share manipulated or forged pictures for amusing on social media. However, forged pictures could also be employed in several serious cases, like scientific publications and media manipulations [26]. Image forgery detection detects whether an image is original or not, an adequate number of features are needed for the detection.…”

Section: Related Workmentioning

confidence: 99%

“…There are two primary classes of passive authentication techniques: image-splicing techniques and copymove forgery detection techniques [8,9,26]. The primary attempt to detect copy move forgery was delineated in [27].…”

Section: Related Workmentioning

confidence: 99%

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Image forgery detection based on fusion of lightweight deep learning models

Doegar¹,

Hiriyannaiah²,

Matt³

et al. 2021

Turk J Elec Eng & Comp Sci

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Image forgery detection is one of the key challenges in various real time applications, social media and online information platforms. The conventional methods of detection based on the traces of image manipulations are limited to the scope of predefined assumptions like hand-crafted features, size and contrast. In this paper, we propose a fusion based decision approach for image forgery detection. The fusion of decision is based on the lightweight deep learning models namely SqueezeNet, MobileNetV2 and ShuffleNet. The fusion decision system is implemented in two phases. First, the pretrained weights of the lightweight deep learning models are used to evaluate the forgery of the images. Secondly, the fine-tuned weights are used to compare the results of the forgery of the images with the pre-trained models. The experimental results suggest that the fusion based decision approach achieves better accuracy as compared to the state-of-the-art approaches.

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“…Due to highly imbalanced datasets 1 , Precision (P), Recall (R) and F 1 -Score (F 1 ) are chosen as criteria for performance evaluation [48]…”

Section: A Evaluation Criteriamentioning

confidence: 99%

An Improved Algorithm for Digital Image Authentication and Forgery Localization Using Demosaicing Artifacts

Retraint

2019

IEEE Access

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This paper focuses on the digital image authentication and forgery localization using demosaicing artifacts. The aim is to build an algorithm allowing a bridge between the color filter array pattern and demosaicing algorithm estimation, and the statistical analysis of demosaicing artifacts in spatial domain to improve the authentication and localization performance. After analyzing the evolution of demosaicing traces in camera acquisition pipeline, a robust feature statistic characterizing demosaiced digital images is first developed on the basis of the noise residue of green channel. Such a feature statistic is less sensitive to the edges problem because only the smooth region of green channel is used in the development. Next, a single normal mixture model is proposed to describe the probability distribution of feature statistics for both original and tampered images. Therefore, normality tests can be used to authenticate automatically digital images. The authentication performance can be further improved by human interpretation of supported graphic tools. Finally, a penalized expectation-maximization algorithm is used to localize forged areas in tampered images. Numerous comparative studies on four well-known datasets show that the developed algorithm yields better performance and robustness than existing forensics algorithms of the same kind. INDEX TERMSDemosaicing traces, digital image authentication, forgery localization, normal mixture model, penalized expectation-maximization algorithm. NHAN LE received the Engineering degree in industrial engineering and management from the

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Evaluation of copy-move forgery detection: datasets and evaluation metrics

Cited by 39 publications

References 52 publications

Cloud‐Based Fusion of Residual Exploitation‐Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Cloud‐Based Fusion of Residual Exploitation‐Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Image forgery detection based on fusion of lightweight deep learning models

An Improved Algorithm for Digital Image Authentication and Forgery Localization Using Demosaicing Artifacts

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