Most digital forensic investigations involve images presented as evidence. One of the common problems of these investigations is to prove the image's originality or, as a matter of fact, its manipulation. One of the guaranteed approaches to prove image forgery is JPEG double compressions. Double compression happens if a JPEG image is manipulated and saved again. Thus, the binaries of the image will be changed based on a “previous” quantization table. This paper presents a practical approach to detecting manipulated images using double JPEG compression analysis, implemented in a newly developed software tool. The method relies on an adaptive database of quantization tables, which stores all possible tables and generates new ones based on varying quality factors of recognized tables. The detection process is conducted through image metadata extraction, allowing analysis without the need for the original non-manipulated image. The tool analyzes the suspected image using chrominance, and luminance quantization tables utilizing the jpegio Python library. The tool recognizes camera sources as well as the programs used for manipulating images with the related compression rate. The tool has demonstrated effectiveness in identifying image manipulation, providing a useful tool for digital forensic investigations. The tool identified 96% of modified images whereas the other 4% identified as false positives. The tool fixes the false positives by extracting the software information from the image metadata. With a rich sources database, forensic examiners can use the proposed tool to detect manipulated evidence images using the evidence image only.  

The Internet of Things (IoT) driven Industrial Revolution 4.0 (IR4.0) and this is impacting every sector of the global economy. With IoT devices, everything is computerized. Today's digital forensics is no longer limited to computers, mobiles, or networks. The current digital forensics landscape demands a significantly different approach. The traditional digital forensics frameworks no longer meet the current requirements. Therefore, in this paper, we propose a novel framework called “Multi-level Artifact of Interest Digital Forensics Framework for IoT” (MAoIDFF-IoT). The keynote "Multi-level" aims to cover all levels of the IoT architecture. Our novel IoT digital forensics framework focuses on the Artifact of Interest (AoI). Additionally, it proposes the action/detection matrix. It encompasses the advantages of the previous frameworks while introducing new features specifically designed to make the framework suitable for current and future IoT investigation scenarios. The MAoIDFF-IoT framework is designed to face the challenges of IoT forensic analysis and address the diverse architecture of IoT environments. Our proposed framework was evaluated through real scenario experiments. The evaluation of the experimental results reveals the superiority of our framework over existing frameworks in terms of usability, inclusivity, focus on the (AoI), and acceleration of the investigation process.