# Browser Forensic Investigations of Instagram Utilizing IndexedDB Persistent Storage
## 2 Related Works
The images sent from the Instagram application were successfully intercepted in the network traffic. This paper covers only the Android application of Instagram and not information about persistent storage analysis. [PERSON] and [PERSON] [17] investigated efficient techniques to eliminate challenges in forensic analysis of Instagram and Path social media applications on iPhone devices. The process included a complete process of data population, image acquisition, and automatic examination. It is concluded that the internal memory of the iPhone 5s device contained artifacts from the social media applications, even though they are not installed in the internal memory. [PERSON] and [PERSON] [18] particularized the mobile investigations of Instagram on Android devices. The research applied the investigation stages of the Digital Forensics Research Workshop (DFRWS) with the utilization of the OXYGEN mobile forensics tool and JSON Viewer. The paper concludes on the sufficiency of the forensic tools to obtain artifacts from chat sessions that are extracted from both internal and external memory. The paper presents a systematic look into Android device investigations of Instagram. However, the shared methodology is slightly short on detail to provide an extension to other platforms. [PERSON] et al. [19] called attention to information exposure on Instagram pages with potential threats such as impersonation, fraud, and copyright violation. A reverse-engineering method is applied to obtain personal information from Instagram pages and data sources such as cache, SQLite files. Several tools including ADB (Android Debug Bridge) and DB Browser for SQLite were utilized during the examination. It is concluded that the user behavior on Instagram can be analyzed by arbitrary users with little effort. The applied method is also suggested as a digital forensic examination technique. [PERSON] [20] conducted a detailed investigation on the images handled by Instagram. Various best practices such as the selection of image quality during extraction are shared to provide a framework for obtaining reliable data. A cross-platform experiment, questioning how an image from an Android device would look when uploaded to an Apple device, is made available as Supplementary Data. As the primary outcome, the essentials of a set of techniques to identify whether an image is original and authentic or downloaded from Instagram are laid out. Additionally, the characteristics of Instagram processed images are presented as a framework for future research. [PERSON] et al. [21] analyzed the impersonators targeting Instagram users to construct a technique of impersonation identification. A large amount of data are utilized with both fake and genuine accounts, where users were divided into clusters based on their profile characteristics. The paper presents findings that indicate a considerable amount of political interest in bot-like clusters. It is also discussed that the interest levels of bot-like clusters in news agencies and sports stars were not significantly different. [PERSON] and [PERSON] [22] focused on the Instagram artifacts of Android in vanish mode, where the messages, memes, and pictures disappear when the chat is closed. The question of whether the vanished messages could be obtained by forensic investigators is addressed. Artifacts are extracted from sources such as direct.db and rendered videos from the Android file storage location of Instagram. It is discussed that messages, reels, videos, and some other media items can be uncovered from the direct.db-wal file. The study is focused exclusively on the Android artifacts, and IndexedDB storage of web browsers is not addressed. [PERSON] et. al. [23] focused on Instagram photos and their sensor pattern noise (SPN), which is described as a feasible device fingerprint. Two groups of filters were applied, where SPN was either attenuated or preserved. About twenty-thousand images were experimented on, and over 96% of accuracy was achieved in differentiating filters.