Beyond Pixels: Image Provenance Analysis Leveraging Metadata

Abstract

Creative works, whether paintings or memes, follow unique journeys that result in their final form. Understanding these journeys, a process known as "provenance analysis," provides rich insights into the use, motivation, and authenticity underlying any given work. The application of this type of study to the expanse of unregulated content on the Internet is what we consider in this paper. Provenance analysis provides a snapshot of the chronology and validity of content as it is uploaded, re-uploaded, and modified over time. Although still in its infancy, automated provenance analysis for online multimedia is already being applied to different types of content. Most current works seek to build provenance graphs based on the shared content between images or videos. This can be a computationally expensive task, especially when considering the vast influx of content that the Internet sees every day. Utilizing non-contentbased information, such as timestamps, geotags, and camera IDs can help provide important insights into the path a particular image or video has traveled during its time on the Internet without large computational overhead. This paper 1 tests the scope and applicability of metadata-based inferences for provenance graph construction in two different scenarios: digital image forensics and cultural analytics.

Challenges for Automatic Detection of Fake News Related to Migration : Invited paper

“…The robustness of the resulting models and their ability to generalize to further domains is thus very limited. Datasets like the ISOT Fake News Dataset 15 , FNID 16 and LIAR 17 are based on PolitiFact. FakeNewsNet 18 also uses PolitiFact 19 in combination with GossipCop 20 .…”

“…The paper also proposes a dataset mined from Reddit. [17] address the scalability issue by inferring possible provenance relations from metadata and thus significantly reduce the number of content matches that need to be performed. For video, this problem (also known as nearduplicate video retrieval) is even more computationally demanding [18].…”

Media Forensics Challenge image provenance evaluation and state-of-the-art analysis on large-scale benchmark datasets

“…Pinto et al [10] present a provenance filtering method to improve retrieval of candidate images by incorporating the context of top results. More recently, Bharati et al [11] design an IPA approach that utilizes commonly present file metadata tags, e.g., date, location, camera, editing, and thumbnail related metadata. Zhang et al [8] demonstrate an approach to learn a pairwise ancestor-offspring classifier for detecting related images, as well as a graph building algorithm that combines local feature matching and pixel similarity scores.…”

“…Recently, researchers began to actively study IPA, and a few approaches are introduced in [5,[8][9][10][11]. But the IPA systems still have much room to improve their performance.…”

Visual Analysis of Spatial Metadata

“…The Spatial data mining techniques used so far in different fields are incapable in representing the complete metadata descriptions of the geospatial dataset [8]. Earlier different data mining techniques, statistical techniques, geographical and cartographic techniques are used for retrieval of spatial data but access to spatial data quality information were problematic [9], so for better understanding and efficient use of spatial metadata we will use visual data exploration techniques to explore the geographical metadata, where a visualization will enhance communication between the user and the computer. The visualization techniques are classified into dense pixel display, iconic display, standard 2D/3D display, and interaction and distortion techniques [10].…”