Spotting the difference: Context retrieval and analysis for improved forgery detection and localization

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

doi:10.1109/icip.2017.8297049

Cited by 9 publications

(10 citation statements)

References 33 publications

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“…However, the problem of IPA is not intensively studied so far in the literature. The research areas that are related to IPA are near duplicate detection [22,23], and image splicing detection [24][25][26][27][28][29]. Most of these works are designed to classify whether a candidate image is a near duplicate to a given query image.…”

Section: Related Workmentioning

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

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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%

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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“…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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“…In contrast to those abovementioned blind-splicing localization methods, context-based search-and-compare approaches were proposed in References [17,18] to localize spliced regions. Specifically, a spliced image was used as a query image in the problem of image retrieval among the database of authentic 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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Spotting the difference: Context retrieval and analysis for improved forgery detection and localization

Cited by 9 publications

References 33 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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