Digital Signature Scheme for Image

Doke, Kanchan; Patil, Shankar M.

doi:10.5120/7708-1012

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Doke et al [ 20 ] proposed a low phase filter method for acquiring an X random pattern for each random matrix recurrently. Through the signing operation on the image, the model generates a digital signature.…”

Section: Classification Of Active Image Forgery Detectionmentioning

confidence: 99%

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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Section: Classification Of Active Image Forgery Detectionmentioning

confidence: 99%

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 signature is generated through a system by placing process of signing in image which is digital [3]. The steps involved in image signing process are [4]: 1) The image is decomposed by using the parameterized wavelet feature. 2) SDS is extracted 3) Hash cryptographically extract SDS and generate signature of crypto by using sender key which is private of an image.…”

Section: Active Approachmentioning

confidence: 99%

Image Forgery Detection Using Dct And Quantization Matrix Techniques

Abraham*,

Rodrigues,

Fernandes

2019

IJEAT

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In today’s era the image has become useful for communication purpose. But due to the development of software and various techniques it is possible to change images in adding or removing essential feature from it without leaving a clue of real image. It is not easy for the common people to identify whether the image original or tampered. In order to avoid this problem, forgery detection came into existence. Detection of forgery refers to task of image processing to identify that the images are unique or tampered. Several techniques have been used in order to detect the forgeries from the forged image, but this issue has not yet solved. In order to solve these issues we have used Discrete Cosine Transformation (DCT) and quantization matrix techniques for identifying forged areas of image, where the quality of image is not reduced. The Discrete Cosine Transformation (DCT) is used in order for characterizing the overlapping blocks and quantization matrix is used to compress DCT values and gives both highly compressed and best decompressed image quality. Here we use block matching algorithm. This algorithm one of the most frequently used for detecting image which is duplicate. This proposed work also supports for different kinds of images such as JPEG, JPG or PNG of any size it can be either mxn or nxn.

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“…System generate digital signature by applying signing process on digital image. Image Signing process contain following steps [3]: 1) Decompose the image using parameterized wavelet feature. 2) Extract the SDS 3) Cryptographically hash the extracted SDS, generate the crypto signature by the image senders private key.…”

Section: ) Digital Signaturementioning

confidence: 99%