{"title":"Encrochat: The hacker with a warrant and fair trials?","authors":"Radina Stoykova","doi":"10.2139/ssrn.4374363","DOIUrl":"https://doi.org/10.2139/ssrn.4374363","url":null,"abstract":"","PeriodicalId":116810,"journal":{"name":"Forensic Sci. Int. Digit. Investig.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124623936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.48550/arXiv.2303.00810
Arianna Trozze, Toby P Davies, Bennett Kleinberg
Fraud across the decentralized finance (DeFi) ecosystem is growing, with victims losing billions to DeFi scams every year. However, there is a disconnect between the reported value of these scams and associated legal prosecutions. We use open-source investigative tools to (1) investigate potential frauds involving Ethereum tokens using on-chain data and token smart contract analysis, and (2) investigate the ways proceeds from these scams were subsequently laundered. The analysis enabled us to (1) uncover transaction-based evidence of several rug pull and pump-and-dump schemes, and (2) identify their perpetrators' money laundering tactics and cash-out methods. The rug pulls were less sophisticated than anticipated, money laundering techniques were also rudimentary and many funds ended up at centralized exchanges. This study demonstrates how open-source investigative tools can extract transaction-based evidence that could be used in a court of law to prosecute DeFi frauds. Additionally, we investigate how these funds are subsequently laundered.
{"title":"Of Degens and Defrauders: Using Open-Source Investigative Tools to Investigate Decentralized Finance Frauds and Money Laundering","authors":"Arianna Trozze, Toby P Davies, Bennett Kleinberg","doi":"10.48550/arXiv.2303.00810","DOIUrl":"https://doi.org/10.48550/arXiv.2303.00810","url":null,"abstract":"Fraud across the decentralized finance (DeFi) ecosystem is growing, with victims losing billions to DeFi scams every year. However, there is a disconnect between the reported value of these scams and associated legal prosecutions. We use open-source investigative tools to (1) investigate potential frauds involving Ethereum tokens using on-chain data and token smart contract analysis, and (2) investigate the ways proceeds from these scams were subsequently laundered. The analysis enabled us to (1) uncover transaction-based evidence of several rug pull and pump-and-dump schemes, and (2) identify their perpetrators' money laundering tactics and cash-out methods. The rug pulls were less sophisticated than anticipated, money laundering techniques were also rudimentary and many funds ended up at centralized exchanges. This study demonstrates how open-source investigative tools can extract transaction-based evidence that could be used in a court of law to prosecute DeFi frauds. Additionally, we investigate how these funds are subsequently laundered.","PeriodicalId":116810,"journal":{"name":"Forensic Sci. Int. Digit. Investig.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134378637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.48550/arXiv.2212.08035
Sean McKeown, W. Buchanan
Content-based file matching has been widely deployed for decades, largely for the detection of sources of copyright infringement, extremist materials, and abusive sexual media. Perceptual hashes, such as Microsoft's PhotoDNA, are one automated mechanism for facilitating detection, allowing for machines to approximately match visual features of an image or video in a robust manner. However, there does not appear to be much public evaluation of such approaches, particularly when it comes to how effective they are against content-preserving modifications to media files. In this paper, we present a million-image scale evaluation of several perceptual hashing archetypes for popular algorithms (including Facebook's PDQ, Apple's Neuralhash, and the popular pHash library) against seven image variants. The focal point is the distribution of Hamming distance scores between both unrelated images and image variants to better understand the problems faced by each approach.
{"title":"Hamming Distributions of Popular Perceptual Hashing Techniques","authors":"Sean McKeown, W. Buchanan","doi":"10.48550/arXiv.2212.08035","DOIUrl":"https://doi.org/10.48550/arXiv.2212.08035","url":null,"abstract":"Content-based file matching has been widely deployed for decades, largely for the detection of sources of copyright infringement, extremist materials, and abusive sexual media. Perceptual hashes, such as Microsoft's PhotoDNA, are one automated mechanism for facilitating detection, allowing for machines to approximately match visual features of an image or video in a robust manner. However, there does not appear to be much public evaluation of such approaches, particularly when it comes to how effective they are against content-preserving modifications to media files. In this paper, we present a million-image scale evaluation of several perceptual hashing archetypes for popular algorithms (including Facebook's PDQ, Apple's Neuralhash, and the popular pHash library) against seven image variants. The focal point is the distribution of Hamming distance scores between both unrelated images and image variants to better understand the problems faced by each approach.","PeriodicalId":116810,"journal":{"name":"Forensic Sci. Int. Digit. Investig.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114506083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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