An Evaluation of Digital Image Forgery Detection Approaches

Kashyap, Abhishek; Parmar, Rajesh Singh; Agrawal, Megha; Gupta, Harsh

doi:10.48550/arxiv.1703.09968

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…A number of techniques have been developed in the literature for addressing the problem of protecting images against pixel tampering and forgery [18]. One of the most well-known techniques is watermarking-based algorithms [19][20].…”

Section: Triple Hybrid Security Approachmentioning

confidence: 99%

Image Protection Against Forgery and Pixel Tampering Based on a Triple Hybrid Security Approach

Nagm¹,

Torky²,

Ghazala³

et al. 2019

Advances in Intelligent Systems and Computing

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Due to the widespread of advanced digital imaging devices, forgery of digital images became more serious attack patterns. In this attack scenario, the attacker tries to manipulate the digital image to conceal some meaningful information of the genuine image for malicious purposes. This leads to increase security interest about protecting images against integrity tampers. This paper proposes a novel technique for protecting colored images against forgery and pixel tamper. The proposed approach is designed as a hybrid model from three security techniques, Message Digest hashing algorithm (MD5), Advanced Encryption Standard-128 bits (AES), and Stenography. The proposed approach has been evaluated using set of image quality metrics for testing the impact of embedding the protection code on image quality. The evaluation results proved that protecting image based on Least Significant Bit (LSB) is the best technique that keep image quality compared with other two bit-substitution methods. Moreover, the results proved the superiority of the proposed approach compared with other technique in the literature.

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Section: Triple Hybrid Security Approachmentioning

confidence: 99%

Image Protection Against Forgery and Pixel Tampering Based on a Triple Hybrid Security Approach

Nagm¹,

Torky²,

Ghazala³

et al. 2019

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

Sharma

Kumar²,

Sharma³

2022

Multimed Tools Appl

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The digital image proves critical evidence in the fields like forensic investigation, criminal investigation, intelligence systems, medical imaging, insurance claims, and journalism to name a few. Images are an authentic source of information on the internet and social media. But, using easily available software or editing tools such as Photoshop, Corel Paint Shop, PhotoScape, PhotoPlus, GIMP, Pixelmator, etc. images can be altered or utilized maliciously for personal benefits. Various active, passive and other new deep learning technology like GAN approaches have made photo-realistic images difficult to distinguish from real images. Digital image tamper detection now focuses on determining the authenticity and consistency of digital photos. The major research problems use generic solutions and strategies, such as standardized data sets, benchmarks, evaluation criteria and generalized approaches.This paper overviews the evaluation of various image tamper detection methods. A brief discussion of image datasets and a comparative study of image criminological (forensic) methods are included in this paper. Furthermore, recently developed deep learning techniques along with their limitations have also been addressed. This study aims to comprehensively analyze image forgery detection methods using conventional and advanced deep learning approaches.

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An Evaluation of Digital Image Forgery Detection Approaches

Cited by 2 publications

References 36 publications

Image Protection Against Forgery and Pixel Tampering Based on a Triple Hybrid Security Approach

Image Protection Against Forgery and Pixel Tampering Based on a Triple Hybrid Security Approach

Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation

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