A Forensic Methodology for Detecting Image Manipulations

TL;DR

This paper presents a forensic methodology combining image metadata analysis and mobile artifact examination to detect image manipulations, enhancing accuracy and reducing false positives in digital investigations.

Contribution

It introduces a novel approach that integrates metadata parsing, a reference database, and mobile artifact analysis, surpassing traditional graphic feature-based methods.

Findings

Effective detection of manipulated images using metadata comparison.

Reduction in false positives compared to existing methods.

Provision of open-source tools and datasets for further research.

Abstract

By applying artificial intelligence to image editing technology, it has become possible to generate high-quality images with minimal traces of manipulation. However, since these technologies can be misused for criminal activities such as dissemination of false information, destruction of evidence, and denial of facts, it is crucial to implement strong countermeasures. In this study, image file and mobile forensic artifacts analysis were conducted for detecting image manipulation. Image file analysis involves parsing the metadata of manipulated images (e.g., Exif, DQT, and Filename Signature) and comparing them with a Reference DB to detect manipulation. The Reference DB is a database that collects manipulation-related traces left in image metadata, which serves as a criterion for detecting image manipulation. In the mobile forensic artifacts analysis, packages related to image editing tools were extracted and analyzed to aid the detection of image manipulation. The proposed methodology overcomes the limitations of existing graphic feature-based analysis and combines with image processing techniques, providing the advantage of reducing false positives. The research results demonstrate the significant role of such methodology in digital forensic investigation and analysis. Additionally, We provide the code for parsing image metadata and the Reference DB along with the dataset of manipulated images, aiming to contribute to related research.