Provenance filtering for multimedia phylogeny

Pinto, Allan; Moreira, Daniel; Bharati, Aparna; Brogan, Joel; Bowyer, Kevin W.; Flynn, Patrick J.; Scheirer, Walter J.; Rocha, Anderson

doi:10.1109/icip.2017.8296532

Cited by 9 publications

(16 citation statements)

References 21 publications

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“…Bharati et al [9] propose an undirected graph to show the relationships between images based on spatial information given by representative key points and match consistency. 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.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Jin¹,

Lee²,

Fiscus³

et al. 2020

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With the development of storage, transmission, editing, and sharing tools, digital forgery images are propagating rapidly. The need for image provenance analysis has never been more timely. Typical applications are content tracking, copyright enforcement, and forensics reasoning. However, large-scale image provenance datasets, which contain diverse manipulation history graphs with various manipulation operations and rich metadata, are still needed to facilitate the research. It is one of the major factors that hinders the development of techniques for image provenance analysis. To address this issue, we introduce large-scale benchmark datasets for provenance analysis, namely Media Forensics Challenge-Provenance (MFC-Prov) datasets. Two provenance tasks are designed along with evaluation metrics. Furthermore, extensive analysis is conducted for system performance in terms of accuracy on our datasets.

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Section: Related Workmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Jin¹,

Lee²,

Fiscus³

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Following the protocol proposed by NIST [54], we perform both end-to-end and oracle-filter provenance analysis over this dataset. End-to-end analysis requires performing provenance filtering prior to graph construction [58]. In this case, for each query image, graphs are built upon a list of ranked images that might include distractors and miss genuinely related images due to imperfect image filtering.…”

Section: Provenance Analysis For Digital Forensicsmentioning

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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“…In the literature, a few studies were proposed to find the provenance of manipulated images. In Reference [22], the provenance of spliced images was searched in two tiers using Speeded-Up Robust Features. The first tier was responsible to find the host (target) images, and then the search was refined in the second tier to find the donor (source) images.…”

Section: Introductionmentioning

confidence: 99%

“…Provenance graph-construction methods were proposed in References [23,24] to show the relationships among images with multiple manipulation operations, such as splicing, spatial operations and enhancement. The experimental results in References [22][23][24] were performed using only a small number of test images and the results show that their methods achieved moderate retrieval performance for the source images. This paper aims to address the shortcomings of previous works and provide a convincing provenance of spliced images.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Image-Retrieval Method for Image-Splicing Validation

et al. 2019

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Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.

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Provenance filtering for multimedia phylogeny

Cited by 9 publications

References 21 publications

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

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

Beyond Pixels: Image Provenance Analysis Leveraging Metadata

Hybrid Image-Retrieval Method for Image-Splicing Validation

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