Use of discrete wavelet transform method for detection and localization of tampering in a digital medical image

Gadhiya, Tushar; Roy, Anil K.; Mitra, Suman K.; Mall, Vinod

doi:10.1109/tenconspring.2017.8070082

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…Wavelet analysis has been considered as a tool for time series analysis in different research domains, including power system quality analysis [20], signal processing [21], image processing of medical imaging [22], and hydrological time series data analysis [23]. Wavelet analysis provides localized details in time and frequency and can detect changes and provide tools to extract these features.…”

Section: ) Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

A Case Study to Quantify Variability in Building Load Profiles

et al. 2021

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Recent technology development and penetration of advanced metering infrastructure (AMI), advanced building control systems, and the internet-of-things (IoT) in the built environment are providing detailed information on building operation, performance, and user's comfort and behavior. Building owners can obtain a wide range of energy consumption details at various levels of time granularity to augment their decisions as they manage the building operation and interact with the grid. AMI data are providing a new level of detail and visibility that may enhance building services and assets in the smart grid domain and make buildings inch closer to becoming a grid-interactive energy efficient buildings (GEB). While utility-installed AMI typically records energy consumption at a 15, 30, or 60-minute resolution, building-owner-installed metering can record energy consumption at one-minute or sub-minute time scales, providing information about how much the energy consumption varies from one sub-minute to the other (i.e. variability) at a finer time resolutions than typically available from AMI. This paper examines one-minute building load profile data sets and presents a framework to study, define, extract, quantify and analyze variability in buildings' load profiles. The discussion of variability and its analysis is based on a case study of an actual sub-minute time-resolution data set, collected in 2019, for two buildings in a Midwest state in the USA. The result shows that for the case studies, the level of variability in an end-use category is not simply proportional to its consumption. Furthermore, distinct and predictable daily variability patterns emerge in end-use load categories. This information is useful for a host of applications including prediction, forecasting, and modeling.

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Section: ) Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

A Case Study to Quantify Variability in Building Load Profiles

et al. 2021

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“…The outcomes are true to form and the calculation beats the traditional calculations and can be utilized related to them. Gadhiya et al [11] clarifies that the utilization of computerized pictures in the domain of restorative diagnostics has been an extraordinary shelter to the general public and the therapeutic field as there can be a great deal of sicknesses and issues that can be identified now without the utilization of any intrusive methodology. This was beyond the realm of imagination prior as the vast majority of the issues required broad medical procedure and biopsies to determine certain infections.…”

Section: Literature Reviewmentioning

confidence: 99%

Fake colorized and morphed image detection using convolutional neural network

Pillai¹

2020

TIPCV

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As the technology develops, utilization of the phony pictures is at highest, So, as per an overview most of the pictures stored on the server or in the cloud are being transformed or counterfeit. As a result, it is difficult to identify whether the images stored are real or not. So not many systems are existing today which are equipped to tell whether pictures are fake or not. Earlier Histogram based and feature extraction-based methods were used to identify fake images. The neural network is being the most advanced technology distinguishes the phony pictures by studying various features of the image and learning the procedure of faking an image. So as a minor advance towards this, proposed system uses features like dark channel, bright channel, RGB channel and alpha channel. Using Gaussian distribution, we can anlayze the edges of the images to identify phony images. Deep layer analysis is performed using convolutional neural system along with the fuzzy classification procedure to improve the likelihood of recognizing phony pictures. Through this research it is concluded that convolutional neural networks (CNN) based fake colorized image detection showcases a better result than histogram based and feature extraction based fake colorized image detection.

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“…Tushar D. Gadhiya, Anil K. Roy, et al, [7] proposed a method for the detection and localization of tampering by hash based representation and uses discrete wavelet transform. On the other hand, Chun Kiat Tan, et al [8] propose a fully reversible, dual-layer watermarking scheme with tamper detection capability and locate tampered regions in the images [12].…”

Section: Related Workmentioning

confidence: 99%

An Effective Method for Detection and Localization of Tampering

Saju¹

2019

IJISCS

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Digital transmission of sensitive images and documents over unsecure networks, such as the Internet, has become a general practice. As a result, the digital content has become vulnerable to intentional and unintentional modifications during transmission. A system that combines linear interpolation for tamperdetection and localization that provide security to exchanged data is the main concern. The algorithm is based on hash based representation of such image and uses discrete wavelet transform method to carry out detection and localization of tampering. And it is robust against harmless manipulation and sensitive enough for even a minute tampering. The proposed model presents a superior tamper detection and recovery capability in comparison to other models in the related literature.

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Use of discrete wavelet transform method for detection and localization of tampering in a digital medical image

Cited by 10 publications

References 17 publications

A Case Study to Quantify Variability in Building Load Profiles

A Case Study to Quantify Variability in Building Load Profiles

Fake colorized and morphed image detection using convolutional neural network

An Effective Method for Detection and Localization of Tampering

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