  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.
