False Image Injection Prevention Using iChain
Source abstract
Abstract
The advances in information and communication technology are consistently beneficial for the healthcare sector. A trend in the healthcare sector is the progressive shift in how data are acquired and the storage of such data in different facilities, such as in the cloud, due to the efficiency and effectiveness offered. Digital images related to healthcare are sensitive in nature and require maximum security and privacy. A malicious entity can tamper with such stored digital images to mislead healthcare personnel and the consequences of wrong diagnosis are harmful for both parties. A new type of cyber attack, a false image injection attack (FIIA) is introduced in this paper. Existing image tampering detection measures are unable to guarantee tamper-proof medical data in real time. Inspired by the effectiveness of emerging blockchain technology, a security framework, image chain (iChain) is proposed in this paper to ensure the security and privacy of the sensitive healthcare images. The practical challenges associated with the proposed framework and further research that is required are also highlighted.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Media Forensic Detection · Advanced Steganography and Watermarking Techniques · Digital and Cyber Forensics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Academic Centre of Cyber Security Excellence, School of Science, Edith Cowan University, WA 6027, Australia
References (33)
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