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Dual-Module Architecture for Robust Image Forgery Segmentation and Classification Toward Cyber Fraud Investigation
This study presents a dual-module architecture for image forgery detection in the context of cyber fraud investigation, designed to provide interpretable and court-admissible forensic evidence. The forgery segmentation module built on an encoder–decoder structure segments forged regions at the pixel level to produce a binary mask. The forgery classification module with two-stream structure integrates contextual and noise-residual cues from the raw image and the binary mask to determine the designated forgery method. The segmentation module achieves an F1-Score of 0.875 and an IoU of 0.78, while the classification module reaches an F1-Score of 0.94. The combined system attains an end-to-end F1-Score of 0.855 and AUC of 0.91, demonstrating reliable detection performance and enhanced explainability. These results highlight the framework’s potential for forensic image analysis and its practical applicability to real-world cyber fraud investigations.
Journal Article
Managing client emotions in forensic accounting and fraud investigation
Manage client emotions in forensic accounting and fraud investigations While many resources exist that outline the primary functional aspects of conducting a forensic accounting or fraud investigation, this book is the first of its kind in addressing the significance of client emotions during investigations and how important the management of.
eBook
Data Sleuth
Straightforward, practical guidance for working fraud examiners and forensic accountantsIn Data Sleuth: Using Data in Forensic Accounting Engagements and Fraud Investigations, certified fraud examiner, former FBI support employee, private investigator, and certified public accountant Leah Wietholter delivers a step-by-step guide to financial investigation that can be applied to almost any forensic accounting use-case. The book emphasizes the use of best evidence as you work through problem-solving data analysis techniques that address the common challenge of imperfect and incomplete information. The accomplished author bridges the gap between modern fraud investigation theory and practical applications and processes necessary for working practitioners. She also provides: Access to a complimentary website with supplementary resources, including a Fraud Detection Worksheet and case planning templateStrategies for systematically applying the Data Sleuth® framework to streamline and grow your practiceMethods and techniques to improve the quality of your work productData Sleuth is an indispensable, hands-on resource for practicing and aspiring fraud examiners and investigators, accountants, and auditors. It’s a one-of-a-kind book that puts a practical blueprint to effective financial investigation in the palm of your hand.
eBook
Expect to see a lot less cryptocurrency fraud investigations
The Sidebar panel explains why there will be less cryptocurrency fraud investigations under President Donald Trump.
Streaming Video
RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System
In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model.
Journal Article