Bitcoin is the leading cryptocurrency in the world with a total marketcap of nearly USD 33 billion, [1] with 370,000 transactions recorded daily[2]. Pseudo-anonymous, decentralized peer-to-peer electronic cash systems such as Bitcoin have caused a paradigm shift in the way that people conduct financial transactions and purchase goods. Although cryptocurrencies enable users to securely and anonymously exchange money, they can also facilitate illegal criminal activities. Therefore, it is imperative that law enforcement agencies develop appropriate analytical processes that will allow them to identify and investigate criminal activities in the Blockchain (a distributed ledger). In this paper, INTERPOL, through the INTERPOL Global Complex for Innovation, proposes a Bitcoin analytical framework and a software system that will assist law enforcement agencies in the real-time analysis of the Blockchain while providing digital crime analysts with tracing and visualization capabilities. By doing so, it is feasible to render transactions decipherable and comprehensible for law enforcement investigators and prosecutors. The proposed solution is evaluated against three criminal case studies linked to Darknet markets, ransomware and DDoS extortion.

For digital forensic investigations, the proposed scheme verifies the integrity of video contents in legacy surveillance camera systems with no built-in integrity protection. The scheme exploits video frames remaining in slack space of storage media, instead of timestamp information vulnerable to tampering. The scheme is applied to integrity verification of video contents formatted with AVI or MP4 files in automobile blackboxes.

For source camera identification, we propose a method to reconstruct the sensor pattern noise map from a size-reduced query image by minimizing an objective function derived from the observation model. Our method can be applied to multiple queries, and can thus be further improved. Experiments demonstrate the superiority of the proposed method over conventional interpolation-based magnification algorithms.