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.

翻译：通过将人工智能应用于图像编辑技术，如今已能生成高质量图像且仅留下极少的篡改痕迹。然而，由于这些技术可能被滥用于传播虚假信息、销毁证据和否认事实等犯罪活动，因此实施强有力的对策至关重要。本研究开展了图像文件分析与移动取证痕迹分析，以检测图像篡改。图像文件分析包括解析篡改图像的元数据（如Exif、DQT和文件名签名），并将其与参考数据库（Reference DB）进行对比以检测篡改。参考数据库是一个收集图像元数据中与篡改相关痕迹的数据库，作为检测图像篡改的判据。在移动取证痕迹分析中，提取并分析与图像编辑工具相关的软件包，以辅助检测图像篡改。所提出的方法克服了现有基于图形特征分析的局限性，并结合图像处理技术，具有降低误报率的优势。研究结果表明，此类方法在数字取证调查与分析中具有重要作用。此外，我们提供了用于解析图像元数据的代码、参考数据库以及篡改图像数据集，旨在为相关研究做出贡献。