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Conference proceeding
Digital Evidence Acquisition and Deepfake Detection with Decentralized Applications
PEARC '22: Practice and Experience in Advanced Research Computing 2022: Revolutionary: Computing, Connections, You, 87
PEARC '22: Revolutionary: Computing, Connections, You (Boston, Massachusetts, USA, 07/10/2022–07/14/2022)
07/22/2022
Web of Science ID: WOS:001051711300033
Abstract
EXCERPT: Given the rise of digital technology and communication, there’s a higher chance of smartphones containing shreds of evidence related to an incident. The variety of digital evidence sources, creation and sharing of information, and incidents within forums, and other Online broadcasting medium poses new and challenging problems for digital investigators. Three of the most significant obstacles are as follows: 1) Authentication of the evidence, 2) Acquisition 3) Storage and analysis. Blockchain, by offering a decentralized
network and an IPFS hash storage system, can be a great solution to the acquisition and storage challenges. Machine learning (ML), as one of the leading solutions to the identification and authentication of evidence, can provide the best performance in the detection of deepfake media. Our proposed framework, by combining machine learning and the decentralized nature of Dapps is designed to offer authenticity, immutability, traceability, robustness, and distributed trust between evidence entitles and examiners. To be able to keep the storage cost and resources minimal, avoid the whole process of consent/warrant form, extract the relevant data only, our implementation is based on the assumption of voluntary media upload
by those who were present at the crime scene.
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Details
- Title
- Digital Evidence Acquisition and Deepfake Detection with Decentralized Applications
- Publication Details
- PEARC '22: Practice and Experience in Advanced Research Computing 2022: Revolutionary: Computing, Connections, You, 87
- Resource Type
- Conference proceeding
- Conference
- PEARC '22: Revolutionary: Computing, Connections, You (Boston, Massachusetts, USA, 07/10/2022–07/14/2022)
- Publisher
- Associated Computing Machinery (ACM)
- Number of pages
- 2
- Copyright
- © 2022 Copyright held by the owner/author(s).
- Identifiers
- WOS:001051711300033; 99380570795106600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Cybersecurity and Information Technology
- Language
- English