RETRACTED ARTICLE: A comprehensive survey of detecting tampered images and localization of the tampered region

Anbu, T.; Joe, M. Milton; Murugeswari, G.

doi:10.1007/s11042-020-09585-z

Cited by 8 publications

(2 citation statements)

References 53 publications

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“…These visually impaired finders are assembled utilizing AI, for example, to find a little bit change between the cover and stego image or objects a type of classifier or mechanism is prepared. Many tools are used by attackers to identify the basics highpoint of cover steganography [122], [123] and the spatial rich model SRM [124], a list of capabilities named discrete cosine change leftover (DCTR) was proposed [125] for steganalysis of JPEG pictures where the discovery accuracy is assessed utilizing the negligible complete mistake likelihood under equivalent priors and is given by the following formula:…”

Section: B Vulnerabilities To Cover Steganography or Extensive Stegan...mentioning

confidence: 99%

A Comprehensive Study of Digital Image Steganographic Techniques

et al. 2023

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Steganography surpasses other mechanisms of securing data from potential threats. The modern digital arena calls for robust information hiding techniques and, thus, it has always been a flash point for researchers and academicians. Nowadays, transmission is susceptible to numerous hacks while sharing secret information through typical correspondence channel. Accordingly, everybody needs the classification, respectability, and realness of his or her privileged information. Particularly, different techniques are used to take on these security issues like advanced declaration, computerized mark, and cryptography. Nevertheless, these strategies alone cannot be negotiated. Steganography is a revolution where current information compression, data hypothesis, spread range, and cryptography advancements are integrated to meet the requirements for protection of data over the Internet. This study investigates and critically analyses various existing cover steganography techniques and identifies the valuable region that everyone can be benefited. Moreover, we present a comprehensive overview of the fundamental concepts with in the domain of the steganographic methods and steganalysis. These systems have been depicted in numerous areas of the steganography such as spatial space, transform domain, and adaptive space. Moreover, each space has its particular traits. A few regularly involved techniques for improving the steganographic security and upgrading steganalysis capacity are elaborated, summed up; and conceivable examination patterns are talked about. We also systematically separate different methodologies in our review and show their pros and cons, qualities, challenges and significance.INDEX TERMS Steganography, data concealing, cover objects, image quality assessment metrics.

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Section: B Vulnerabilities To Cover Steganography or Extensive Stegan...mentioning

confidence: 99%

A Comprehensive Study of Digital Image Steganographic Techniques

et al. 2023

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“…Image tampering detection technology is currently mainly divided into active detection technology and passive detection technology. [4][5][6] Active detection technology requires watermarking or digital signature processing in advance, while passive detection technology does not perform any pre-processing, and directly analyzes and collects evidence based on the characteristics of the image to be detected. Passive detection technology is recognized as a method of blind forensics, with better adaptability, wider application range, and higher research value.…”

Section: Introductionmentioning

confidence: 99%

An Image Tampering Detection Algorithm of Qualification Certificate Based on CNN and SVM

2021

AJCIS

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With the high speed development of digital image processing technology, the phenomenon of malicious tampering with certificate images is rampant. Facing the problems of difficult human eye recognition of tampering traces and poor detection and classification effect of CNN model, a tampering detection algorithm based on CNN combined with SVM is proposed. The algorithm first performs data preprocessing of image compression, difference, resise, and normalization on the image. Then it uses the CNN model to extract the tamper feature, finally completes the calculation and classification of the fully connected layer feature data through SVM, and builds a certified image CNN+SVM model with tamper detection capability. Experimental results show that comparing with the traditional human eye recognition method and the existing deep learning CNN model, the algorithm can reach a detection accuracy of 48.09%, 91.79 %, and 98.67% under the self-built data set of tampering with certificate images. The detection accuracy rate is higher than the previous two detection accuracy rates.

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A comprehensive survey on image authentication for tamper detection with localization

Chennamma

Madhushree

2022

Multimed Tools Appl

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RETRACTED ARTICLE: A comprehensive survey of detecting tampered images and localization of the tampered region

Cited by 8 publications

References 53 publications

A Comprehensive Study of Digital Image Steganographic Techniques

A Comprehensive Study of Digital Image Steganographic Techniques

An Image Tampering Detection Algorithm of Qualification Certificate Based on CNN and SVM

A comprehensive survey on image authentication for tamper detection with localization

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