Forensic investigation of OOXML format documents

MS Office documents could be illegally copied by offenders, and forensic investigators still face great difficulty in investigating and tracking the source of these illegal copies. This paper mainly proposes a forensic method based on the unique value of the revision identifier (RI) to determine the source of suspicious electronic documents. This method applies to electronic documents which use Office Open XML (OOXML) format, such as MS Office 2007, Mac Office 2008 and MS Office 2010. According to the uniqueness of the RI extracted from documents, forensic investigators can determine whether the suspicious document and another document are from the same source. Experiments demonstrate that, for a copy of an electronic document, even if all the original characters are deleted or formatted by attackers, forensic examiners can determine that the copy and the original document are from the same source through detecting the RI values. Additionally, the same holds true if attackers just copy some characters from the original document to a newly created document. As long as there is one character left whose original format has not been cleared, forensic examiners can determine that the two documents are from the same source using the same method. This paper also presents methods for OOXML format files to detect the time information and creator information, which can be used to determine who the real copyright holder is when a copyright dispute occurs.     

Relevance analysis using revision identifier in MS word

Electronic documents often contain personal or confidential information, which can be used as valuable evidence in criminal investigations. In the digital investigation, special techniques are required for grouping and screening electronic documents, because it is challenging to analyze relationships between numerous documents in storage devices manually. To this end, although techniques such as keyword search, similarity search, topic modeling, metadata analysis, and document clustering are continually being studied, there are still limitations for revealing the relevance of documents. Specifically, metadata used in previous research are not always values present in the documents, and clustering methods with specific keywords may be incomplete because text‐based contents (including metadata) can be easily modified or deleted by users. In this work, we propose a novel method to efficiently group Microsoft Office Word 2007+ (MS Word) files by using revision identifier (RSID). Through a thorough understanding of the RSID, examiners can predict organizations to which a specific user belongs, and further, it is likely to discover unexpected interpersonal relationships. An experiment with a public dataset (GovDocs) provides that it is possible to categorize documents more effectively by combining our proposal with previously studied methods. Furthermore, we introduce a new document tracking method to understand the editing history and movement of a file, and then demonstrate its usefulness through an experiment with documents from a real case.