Toward image phylogeny forests: Automatically recovering semantically similar image relationships

Dias, Zanoni; Goldenstein, Siome; Rocha, Anderson

doi:10.1016/j.forsciint.2013.05.002

Cited by 37 publications

(40 citation statements)

References 33 publications

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“…We make the same assumption in this paper. Other works exist that consider the presence of multiple root nodes resulting in Image Phylogeny Forests [16], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

Face Phylogeny Tree Using Basis Functions

Banerjee

Ross

2020

IEEE Trans. Biom. Behav. Identity Sci.

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Photometric transformations, such as brightness and contrast adjustment, can be applied to a face image repeatedly creating a set of near-duplicate images. Identifying the original image from a set of such near-duplicates and deducing the relationship between them are important in the context of digital image forensics. This is commonly done by generating an image phylogeny treea hierarchical structure depicting the relationship between a set of near-duplicate images. In this work, we utilize three different families of basis functions to model pairwise relationships between near-duplicate images. The basis functions used in this work are orthogonal polynomials, wavelet basis functions and radial basis functions. We perform extensive experiments to assess the performance of the proposed method across three different modalities, namely, face, fingerprint and iris images; across different image phylogeny tree configurations; and across different types of photometric transformations. We also utilize the same basis functions to model geometric transformations and deep-learning based transformations. We also perform extensive analysis of each basis function with respect to its ability to model arbitrary transformations and to distinguish between the original and the transformed images. Finally, we utilize the concept of approximate von Neumann graph entropy to explain the success and failure cases of the proposed IPT generation algorithm. Experiments indicate that the proposed algorithm generalizes well across different scenarios thereby suggesting the merits of using basis functions to model the relationship between photometrically and geometrically modified images. !• Both the authors are with the

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“…We make the same assumption in this paper. Other works exist that consider the presence of multiple root nodes resulting in Image Phylogeny Forests [16], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

Face Phylogeny Tree Using Basis Functions

Banerjee

Ross

2020

IEEE Trans. Biom. Behav. Identity Sci.

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“…However, provenance analysis for online multimedia has not been as extensively studied in the existing literature. The types of work most relevant and related to the problem of image provenance analysis come from three established concepts in the digital forensics literature: near-duplicate detection [19,41], image splicing detection [21,6,38,17,34,15] and image phylogeny [24,23,22]. Most of the proposed methods work towards classifying whether an image is a near-duplicate of the query image in a retrieval context and do not determine the original image among the set of the near-duplicates.…”

Section: Related Workmentioning

confidence: 99%

Beyond Pixels: Image Provenance Analysis Leveraging Metadata

Bharati

Moreira

Brogan

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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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.

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“…The Multimedia Phylogeny term was introduced by Dias et al [8] in their work with image phylogeny tree reconstruction with near-duplicate images. Subsequent work introduced solutions for multiple trees [1] and multiple trees with semantically-similar images [2]. None of the works, however, considered images with multiple parents, and so their solutions were in the form of trees.…”

Section: Related Workmentioning

confidence: 99%

“…Dias et al [1] studied near-duplicate images and proposed a method to find their kinship relationships or the directions of modifications (and transformations) over time, terming such analysis as image phylogeny. Semanticallysimilar images were studied in a follow-up work [2].…”

Section: Introductionmentioning

confidence: 99%

U-Phylogeny: Undirected provenance graph construction in the wild

Bharati

Moreira

Pinto

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

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Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through doctored visual media. Nonetheless, most recent image phylogeny solutions cannot properly address cases of forged composite images with multiple donors, an area known as multiple parenting phylogeny (MPP). This paper presents a preliminary undirected graph construction solution for MPP, without any strict assumptions. The algorithm is underpinned by robust image representative keypoints and different geometric consistency checks among matching regions in both images to provide regions of interest for direct comparison. The paper introduces a novel technique to geometrically filter the most promising matches as well as to aid in the shared region localization task. The strength of the approach is corroborated by experiments with realworld cases, with and without image distractors (unrelated cases).

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Toward image phylogeny forests: Automatically recovering semantically similar image relationships

Cited by 37 publications

References 33 publications

Face Phylogeny Tree Using Basis Functions

Face Phylogeny Tree Using Basis Functions

Beyond Pixels: Image Provenance Analysis Leveraging Metadata

U-Phylogeny: Undirected provenance graph construction in the wild

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