Triage in forensic accounting using Zipf's law

n forensic accounting, use of Benford's law has long been acknowledged as a technique for identifying anomalous numerical data. Zipf's law has received considerably less attention in this domain despite the fact that it is not limited to analysis of numerical datasets. The present paper outlines the context of fraud detection and then describes an experiment that contrasted Benford's law and Zipf's law as highlighters for data anomaly, with a view to enhancing current techniques in fraud detection. Results from tests on two datasets using each technique showed similarities in the samples characterized as 'fraudulent' from which we propose that, when combined with its extended realm of data applicability, Zipf's law has significant potential as an aid to fraud detection as a supplement to other analysis techniques. In particular, this approach could be employed as a component in forensic accounting triage in order to enhance the detection rate of fraud and assist in fraud prevention

A forensic acquisition and analysis system for IaaS

Cloud computing is a promising next-generation computing paradigm that offers significant economic benefits to both commercial and public entities. Furthermore, cloud computing provides accessibility, simplicity, and portability for its customers. Due to the unique combination of characteristics that cloud computing introduces (including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service), digital investigations face various technical, legal, and organizational challenges to keep up with current developments in the field of cloud computing. There are a wide variety of issues that need to be resolved in order to perform a proper digital investigation in the cloud environment. This paper examines the challenges in cloud forensics that are identified in the current research literature, alongside exploring the existing proposals and technical solutions addressed in the respective research. The open problems that need further effort are highlighted. As a result of the analysis of literature, it is found that it would be difficult, if not impossible, to perform an investigation and discovery in the cloud environment without relying on cloud service providers (CSPs). Therefore, dependence on the CSPs is ranked as the greatest challenge when investigators need to acquire evidence in a timely yet forensically sound manner from cloud systems. Thus, a fully independent model requires no intervention or cooperation from the cloud provider is proposed. This model provides a different approach to a forensic acquisition and analysis system (FAAS) in an Infrastructure as a Service model. FAAS seeks to provide a richer and more complete set of admissible evidences than what current CSPs provide, with no requirement for CSP involvement or modification to the CSP’s underlying architecture

Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017