Tampering Detection and Localization in Images from Social Networks: A CBIR Approach

Maigrot, Cédric; Kijak, Ewa; Sicre, Ronan; Claveau, Vincent

doi:10.1007/978-3-319-68560-1_67

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…CBIR systems are commonly designed and implemented for specific domains, such as Medical [Bedo and et. al 2016;Santos et al 2018], agricultural [Ruiz et al 2018], remote sensing [Rosu et al 2017], and social networks [Maigrot et al 2017]. Such lack of a general solution is mainly due to the variety of parameters involved in the search process, e.g., feature vector extractor, distance function, query operator, and relevance feedback technique, which can impact negatively in the quality of CBIR results [Bedo and et.…”

Section: Final Resultsmentioning

confidence: 99%

J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

Novaes¹,

Santos

Carvalho

et al. 2021

jidm

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Similarity searches can be modeled by means of distances following the Metric Spaces Theory and constitute a fast and explainable query mechanism behind content-based image retrieval (CBIR) tasks. However, classical distance-based queries, e.g., Range and k-Nearest Neighbors, may be unsuitable for exploring large datasets because the retrieved elements are often similar among themselves. Although similarity searching is enriched with the imposition of rules to foster result diversification, the fine-tuning of the diversity query is still an open issue, which is is usually carried out with and a non-optimal expensive computational inspection. This paper introduces J-EDA, a practical workbench implemented in Java that supports the tuning of similarity and diversity search parameters by enabling the automatic and parallel exploration of multiple search settings regarding a user-posed content-based image retrieval task. J-EDA implements a wide variety of classical and diversity-driven search queries, as well as many CBIR settings such as feature extractors for images, distance functions, and relevance feedback techniques. Accordingly, users can define multiple query settings and inspect their performances for spotting the most suitable parameterization for a content-based image retrieval problem at hand. The workbench reports the experimental performances with several internal and external evaluation metrics such as P × R and Mean Average Precision (mAP), which are calculated towards either incremental or batch procedures performed with or without human interaction.

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Section: Final Resultsmentioning

confidence: 99%

J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

Novaes¹,

Santos

Carvalho

et al. 2021

jidm

View full text Add to dashboard Cite

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“…We plan to expand the coverage of the image database. We are also exploring ways to improve the content comparison module to eliminate false positives, and to locate modified areas in these images [27]. From an application point of view, the presentation of information to the user must also be studied.…”

Section: Discussionmentioning

confidence: 99%

Fusion-Based Multimodal Detection of Hoaxes in Social Networks

Maigrot

Claveau

Kijak

2018

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

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Social networks make it possible to share information rapidly and massively. Yet, one of their major drawback comes from the absence of verification of the piece of information, especially with viral messages. This is the issue addressed by the participants to the Verification Multimedia Use task of Mediaeval 2016. They used several approaches and clues from different modalities (text, image, social information). In this paper, we explore the interest of combining and merging these approaches in order to evaluate the predictive power of each modality and to make the most of their potential complementarity.

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“…It was also used for the Zhiyuan Fake News Recognition Competition ( Cao et al, 2019 ). D3: The Twitter dataset(D3) was one of the components of MediaEval ( Maigrot, Claveau, Kijak, & Sicre, 2016 ), which was applied to validate the usage task of Multimedia as well as aims to detect the fake multimedia content on social media. The dataset is composed of tweets (short messages posted on Twitter) and each tweet has either text, image/video or social context information.…”

Section: Methodsmentioning

confidence: 99%