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-content-based 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 tests the scope and applicability of metadata-based inferences for provenance graph construction in two different scenarios: digital image forensics and cultural analytics.
 As the volume of digital images available on the Web continues to increase, there is a clear need for more advanced techniques for their effective retrieval and management. In this paper, we present a domain independent framework for both annotating and managing images on the Semantic Web. We introduce a tool that facilitates creating and publishing OWL annotations of image content to the Semantic Web. This is loosely coupled with a Semantic Web portal with provenance tracking. We illustrate the effectiveness of this system with an implementation of the approach and describe a hypothetical use case that resulted in a proof-of-concept designed in collaboration with NASA.     Nowadays, digital content is widespread and also easily redistributable, either lawfully or unlawfully. Images and other digital content can also mutate as they spread out. For example, after images are posted on the Internet, other users can copy, resize and or re-encode them and then repost their versions, thereby generating similar but not identical copies. While it is straightforward to detect exact image duplicates, this is not the case for slightly modified versions. In the last decade, some researchers have successfully focused on the design and deployment of near-duplicate detection and recognition systems to identify the cohabiting versions of a given document in the wild. Those efforts notwithstanding, only recently have there been the first attempts to go beyond the detection of near-duplicates to find the structure of evolution within a set of images. In this paper, we tackle and formally define the problem of identifying these image relationships within a set of near-duplicate images, what we call Image Phylogeny Tree (IPT), due to its natural analogy with biological systems. The mechanism of building IPTs aims at finding the structure of transformations and their parameters if necessary, among a near-duplicate image set, and has immediate applications in security and law-enforcement, forensics, copyright enforcement, and news tracking services. We devise a method for calculating an asymmetric dissimilarity matrix from a set of near-duplicate images and formally introduce an efficient algorithm to build IPTs from such a matrix. We validate our approach with more than 625000 test cases, including both synthetic and real data, and show that when using an appropriate dissimilarity function we can obtain good IPT reconstruction even when some pieces of information are missing. We also evaluate our solution when there are more than one near-duplicate sets in the pool of analysis and compare to other recent related approaches in the literature. A modular drywall is erected wherein a met allic framework is initially formed including spaced vertical rails integrally and orthogonally mounting respective horizontal and vertical rails at upper and lower terminal ends respectively of the vertical rails. Drywall panels are subsequently fastened to forwardly projecting ends of the framework and subsequently filler strips are provided wherein the filler strips are formed to encompass and bridge gaps between drywall panels. The filler strips include a planar forward strip mouting a rearwardly extending longitudinally aligned projecting strip with a magnetic strip mounted to a rear end surface of the projecting strip to secure each filler strip to an associated met allic rail. We propose a new feature-based algorithm to detect image splicings without any prior information. Local features are computed from the co-occurrence of image residuals and used to extract synthetic feature parameters. Splicing and host images are assumed to be characterized by different parameters. These are learned by the image itself through the expectation-maximization algorithm together with the segmentation in genuine and spliced parts. A supervised version of the algorithm is also proposed. Preliminary results on a wide range of test images are very encouraging, showing that a limited-size, but meaningful, learning set may be sufficient for reliable splicing localization.   As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble [1], a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST).   