A scalable Blockchain based system for super resolution images manipulation

Rapuano, Antonio; Iovane, Gerardo; Chinnici, Marta

doi:10.1109/dependsys51298.2020.00011

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…In the future, a data provenance system for user's confidential files using the Ethereum Blockchain has been created. This project [31] tries to address the issue of manipulating "large" photographs up to gig pixels in size. In many cases, the successive elaboration of these images is difficult.…”

Section: Consensusmentioning

confidence: 99%

Using Blockchain to Ensure the Integrity of Digital Forensic Evidence in an IoT Environment

Akhtar¹,

Feng²

2022

EAI Endorsed Transactions on Creative Technologies

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Digital forensics deals with digital evidence. Digital forensics is the study of data detection, acquisition, processing, analysis, and reporting. Encouraging the use of digital forensics in law enforcement investigations. With digital forensics, you can find out what data was taken and how it was copied or spread. Some hackers purposefully destroy data to harm their targets. In other cases, malicious software or hacker involvement can accidentally corrupt vital data. Digital forensics faces challenges of security and integrity. IoT devices can collect digital forensic evidence in an IoT setting, putting cybercrime agencies at danger owing to security and integrity. Many studies have been done recently to improve IoT based digital forensics integrity and security, but researchers face the risk of confidentiality. Recent research shows that digital forensics still faces manipulation and security issues. So a clever and effective approach is needed that not only protects security and integrity but also anticipates threats. So we propose an intelligent and effective solution based on Blockchain and Hashing algorithms. We will store the data collected from IoT devices into Blockchain. Anomalies in the evidence and transactions will be predicted using Machine Learning boosted models. So the proposed model works well because it can predict attacks early on.

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Section: Consensusmentioning

confidence: 99%