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Sensitive media analysis through deep learning architectures

Grant number: 17/16246-0
Support type:Regular Research Grants
Duration: May 01, 2018 - April 30, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Sandra Eliza Fontes de Avila
Grantee:Sandra Eliza Fontes de Avila
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Assoc. researchers:Anderson de Rezende Rocha ; Virginia Nunes Leal Franqueira ; Zanoni Dias


With the growth of video data generated by many devices, such as cameras, smartphones, and CCTVs, allied with the Internet as a fast spreading venue, smart and continuous content filtering becomes paramount. In this context, classification of sensitive media content (e.g., pornography, violence) retains a considerable amount of attention because of its applications: it can be used for detecting, via surveillance cameras, inappropriate behavior; blocking undesired content from being uploaded to general purpose websites (e.g., social networks, online learning platforms, forums), or from being viewed in some places (e.g., schools, workplaces); preventing children from accessing adult content on personal computers, smartphones or smart TVs; and avoiding that improper content is distributed over phones (e.g., sexting). In this project, we aim at designing, developing and deploying solutions for detecting sensitive content in digital images/videos such as pornography, violence and adult content. We will advance the state of the art by investigating deep networks architectures, by proposing new efficient and effective deep learning approaches, and by incorporating mobile solutions for sensitive media analysis. Specifically, we will focus our efforts on: (1) designing and developing novel deep learning approaches for automatically extracting discriminative space-temporal characteristics from videos; and (2) devising new approaches for dealing with sensitive content that incorporate the benefit of robust mid-level representations such as Fisher Vectors with the powerful Deep Learning characterization pipeline. We highlight that our group is at the forefront of such research worldwide being responsible for groundbreaking results associated with digital forensics, machine learning, and computer vision. (AU)

Scientific publications (7)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ALEXANDRINO, ALEXSANDRO OLIVEIRA; LINTZMAYER, CARLA NEGRI; DIAS, ZANONI. Sorting permutations by fragmentation-weighted operations. JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, v. 18, n. 2 APR 2020. Web of Science Citations: 0.
VALLE, EDUARDO; FORNACIALI, MICHEL; MENEGOLA, AFONSO; TAVARES, JULIA; BITTENCOURT, FLAVIA VASQUES; LI, LIN TZY; AVILA, SANDRA. Data, depth, and design: Learning reliable models for skin lesion analysis. Neurocomputing, v. 383, p. 303-313, MAR 28 2020. Web of Science Citations: 0.
SANTOS, THIAGO T.; DE SOUZA, LEONARDO L.; DOS SANTOS, ANDREZA A.; AVILA, SANDRA. Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 170, MAR 2020. Web of Science Citations: 0.
BRITO, KLAIRTON LIMA; JEAN, GERALDINE; FERTIN, GUILLAUME; OLIVEIRA, ANDRE RODRIGUES; DIAS, ULISSES; DIAS, ZANONI. Sorting by Genome Rearrangements on Both Gene Order and Intergenic Sizes. JOURNAL OF COMPUTATIONAL BIOLOGY, v. 27, n. 2 DEC 2019. Web of Science Citations: 0.
MUNOZ, JAVIER VARGAS; GONCALVES, MARCOS A.; DIAS, ZANONI; TORRES, RICARDO DA S. Hierarchical Clustering-Based Graphs for Large Scale Approximate Nearest Neighbor Search. PATTERN RECOGNITION, v. 96, DEC 2019. Web of Science Citations: 0.
OLIVEIRA, ANDRE R.; JEAN, GERALDINE; FERTIN, GUILLAUME; DIAS, ULISSES; DIAS, ZANONI. Super short operations on both gene order and intergenic sizes. Algorithms for Molecular Biology, v. 14, n. 1 NOV 5 2019. Web of Science Citations: 0.
OLIVEIRA, ANDRE RODRIGUES; BRITO, KLAIRTON LIMA; DIAS, ULISSES; DIAS, ZANONI. On the Complexity of Sorting by Reversals and Transpositions Problems. JOURNAL OF COMPUTATIONAL BIOLOGY, v. 26, n. 11, p. 1223-1229, NOV 1 2019. Web of Science Citations: 1.

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