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 researcher: 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

Abstract

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

(14)

(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)

BRITO, KLAIRTON LIMA; ALEXANDRINO, ALEXSANDRO OLIVEIRA; OLIVEIRA, ANDRE RODRIGUES; DIAS, ULISSES; DIAS, ZANONI. Reversals and transpositions distance with proportion restriction. JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, v. 19, n. 4 AUG 2021. Web of Science Citations: 0.

MIRANDA, GUILHERME HENRIQUE SANTOS; ALEXANDRINO, ALEXSANDRO OLIVEIRA; LINTZMAYER, CARLA NEGRI; DIAS, ZANONI. Approximation Algorithms for Sorting lambda-Permutations by lambda-Operations. ALGORITHMS, v. 14, n. 6 JUN 2021. Web of Science Citations: 0.

ALEXANDRINO, ALEXSANDRO OLIVEIRA; MIRANDA, GUILHERME HENRIQUE SANTOS; LINTZMAYER, CARLA NEGRI; DIAS, ZANONI. Length-weighted lambda-rearrangement distance. JOURNAL OF COMBINATORIAL OPTIMIZATION, v. 41, n. 3, p. 579-602, APR 2021. Web of Science Citations: 1.

ALEXANDRINO, ALEXSANDRO OLIVEIRA; OLIVEIRA, ANDRE RODRIGUES; DIAS, ULISSES; DIAS, ZANONI. Genome Rearrangement Distance with Reversals, Transpositions, and Indels. JOURNAL OF COMPUTATIONAL BIOLOGY, v. 28, n. 3, p. 235-247, MAR 1 2021. Web of Science Citations: 0.

RODRIGUES, CAROLINE MAZINI; SORIANO-VARGAS, AUREA; LAVI, BAHRAM; ROCHA, ANDERSON; DIAS, ZANONI. Manifold Learning for Real-World Event Understanding. IEEE Transactions on Information Forensics and Security, v. 16, p. 2957-2972, 2021. Web of Science Citations: 0.

DANTAS, ANA PAULA S.; DE SOUZA, CID C.; DIAS, ZANONI. A heuristic for the convex recoloring problem in graphs. International Transactions in Operational Research, NOV 2020. Web of Science Citations: 0.

ALEXANDRINO, ALEXSANDRO OLIVEIRA; MIRANDA, GUILHERME HENRIQUE SANTOS; LINTZMAYER, CARLA NEGRI; DIAS, ZANONI. Length-weighted lambda-rearrangement distance. JOURNAL OF COMBINATORIAL OPTIMIZATION, v. 41, n. 3 NOV 2020. Web of Science Citations: 0.

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: 4.

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.