Reconstrução de filogenias para imagens e vídeos

Orientadores: Anderson de Rezende Rocha, Zanoni DiasTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Com o advento das redes sociais, documentos digitais (e.g., imagens e vídeos) se tornaram poderosas ferramentas de comunicação. Dada esta nova realidade, é comum esses documentos serem publicados, compartilhados, modificados e republicados por vários usuários em diferentes canais da Web. Além disso, com a popularização de programas de edição de imagens e vídeos, muitas vezes não somente cópias exatas de documentos estão disponíveis, mas, também, versões modificadas das fontes originais (duplicatas próximas). Entretanto, o compartilhamento de documentos facilita a disseminação de conteúdo abusivo (e.g., pornografia infantil), que não respeitam direitos autorais e, em alguns casos, conteúdo difamatório, afetando negativamente a imagem pública de pessoas ou corporações (e.g., imagens difamatórias de políticos ou celebridades, pessoas em situações constrangedoras, etc.). Muitos pesquisadores têm desenvolvido, com sucesso, abordagens para detecção de duplicatas de documentos com o intuito de identificar cópias semelhantes de um dado documento multimídia (e.g., imagem, vídeo, etc.) publicado na Internet. Entretanto, somente recentemente têm se desenvolvido as primeiras pesquisas para ir além da detecção de duplicatas e encontrar a estrutura de evolução de um conjunto de documentos relacionados e modificados ao longo do tempo. Para isso, é necessário o desenvolvimento de abordagens que calculem a dissimilaridade entre duplicatas e as separem corretamente em estruturas que representem a relação entre elas de forma automática. Este problema é denominado na literatura como Reconstrução de Filogenia de Documentos Multimídia. Pesquisas na área de filogenia de documentos multimídia são importantes para auxiliar na resolução de problemas como, por exemplo, análise forense, recuperação de imagens por conteúdo e rastreamento de conteúdo ilegal. Nesta tese de doutorado, apresentamos abordagens desenvolvidas para solucionar o problema de filogenias para imagens e vídeos digitais. Considerando imagens, propomos novas abordagens para tratar o problema de filogenia considerando dois pontos principais: (i) a reconstrução de florestas, importante em cenários onde se tem um conjunto de imagens semanticamente semelhantes, mas geradas por fontes ou em momentos diferentes no tempo; e (ii) novas medidas para o cálculo de dissimilaridade entre as duplicatas, uma vez que esse cálculo afeta diretamente a qualidade de reconstrução da filogenia. Os resultados obtidos com as soluções para filogenia de imagens apresentadas neste trabalho confirmam a efetividade das abordagens propostas, identificando corretamente as raízes das florestas (imagens originais de uma sequencia de evolução) com até 95% de acurácia. Para filogenia de vídeos, propomos novas abordagens que realizam alinhamento temporal nos vídeos antes de se calcular a dissimilaridade, uma vez que, em cenários reais, os vídeos podem estar desalinhados temporalmente, terem sofrido recorte temporal ou serem comprimidos, por exemplo. Nesse contexto, nossas abordagens conseguem identificar a raiz das árvores com acurácia de até 87%Abstract: Digital documents (e.g., images and videos) have become powerful tools of communication with the advent of social networks. Within this new reality, it is very common these documents to be published, shared, modified and often republished by multiple users on different web channels. Additionally, with the popularization of image editing software and online editor tools, in most of the cases, not only their exact duplicates will be available, but also manipulated versions of the original source (near duplicates). Nevertheless, this document sharing facilitates the spread of abusive content (e.g., child pornography), copyright infringement and, in some cases, defamatory content, adversely affecting the public image of people or corporations (e.g., defamatory images of politicians and celebrities, people in embarrassing situations, etc.). Several researchers have successfully developed approaches for the detection and recognition of near-duplicate documents, aiming at identifying similar copies of a given multimedia document (e.g., image, video, etc.) published on the Internet. Notwithstanding, only recently some researches have developed approaches that go beyond the near-duplicate detection task and aim at finding the ancestral relationship between the near duplicates and the original source of a document. For this, the development of approaches for calculating the dissimilarity between near duplicates and correctly reconstruct structures that represent the relationship between them automatically is required. This problem is referred to in the literature as Multimedia Phylogeny. Solutions for multimedia phylogeny can help researchers to solve problems in forensics, content-based document retrieval and illegal-content document tracking, for instance. In this thesis, we designed and developed approaches to solve the phylogeny reconstruction problem for digital images and videos. Considering images, we proposed approaches to deal with the phylogeny problem considering two main points: (i) the forest reconstruction, an important task when we consider scenarios in which there is a set of semantically similar images, but generated by different sources or at different times; and (ii) new measures for dissimilarity calculation between near-duplicates, given that the dissimilarity calculation directly impacts the quality of the phylogeny reconstruction. The results obtained with our approaches for image phylogeny showed effective, identifying the root of the forests (original images of an evolution sequence) with accuracy up to 95%. For video phylogeny, we developed a new approach for temporal alignment in the video sequences before calculating the dissimilarity between them, once that, in real-world conditions, a pair of videos can be temporally misaligned, one video can have some frames removed and video compression can be applied, for example. For such problem, the proposed methods yield up to 87% correct of accuracy for finding the roots of the treesDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2013/05815-2FAPESPCAPE

Reconstructing the nonadaptive radiation of an ancient lineage of ground‐dwelling stick insects (Phasmatodea: Heteropterygidae)

Stick and leaf insects (Phasmatodea) are large terrestrial herbivorous arthropods known for masquerading as plant parts such as bark, twigs and leaves. Their evolutionary history is largely shaped by convergent evolution associated with adaptive radiations on geographically isolated landmasses that have repeatedly generated ground-dwelling ecomorphs. The members of one lineage, however, the Oriental Heteropterygidae, are morphologically rather uniform, and have a predominantly ground-dwelling lifestyle. The phylogeny of Heteropterygidae that comprises approximately 130 described species is controversial and remains uncertain. In particular, the systematic position of the giant Jungle Nymph Heteropteryx dilatata, whose males are capable of flight and exhibit the most plesiomorphic wing morphology among extant phasmatodeans, is of major interest to the scientific community. Here, we analysed a set of seven nuclear and mitochondrial genes to infer the phylogeny of Heteropterygidae covering the group's overall diversity. The divergence time estimation and reconstruction of the historical biogeography resulted in an ancestral distribution across Sundaland with long distance dispersal events to Wallacea, the Philippines and the South Pacific. We were able to resolve the relationships among the three principal subgroups of Heteropterygidae and revealed the Dataminae, which contain entirely wingless small forms, as the sister group of Heteropteryginae + Obriminae. Within Heteropteryginae, Haaniella is recovered as paraphyletic in regard to Heteropteryx. Consequently, Heteropteryx must be considered a subordinate taxon deeply embedded within a flightless clade of stick insects. Within Obriminae, the Bornean Hoploclonia is strongly supported as the earliest diverging lineage. Based on this finding, we recognize only two tribes of equal rank among Obriminae, the Hoplocloniini trib. nov. and Obrimini sensu nov. Within the latter, we demonstrate that previous tribal assignments do not reflect phylogenetic relationships and that a basal splitting event occurred between the wing-bearing clade Miroceramia + Pterobrimus and the remaining wingless Obrimini. The Philippine genus Tisamenus is paraphyletic with regard to Ilocano hebardi, thus, we transfer the latter species to Tisamenus as Tisamenus hebardi comb. nov. and synonymize Ilocano with Tisamenus. We discuss character transformations in the light of the new phylogenetic results and conclude that the current taxonomic diversity appears to be mainly driven by allopatry and not to be the result of niche differentiation. This radiation is thus best described as a nonadaptive radiation

Mandrake : visualizing microbial population structure by embedding millions of genomes into a low-dimensional representation

In less than a decade, population genomics of microbes has progressed from the effort of sequencing dozens of strains to thousands, or even tens of thousands of strains in a single study. There are now hundreds of thousands of genomes available even for a single bacterial species, and the number of genomes is expected to continue to increase at an accelerated pace given the advances in sequencing technology and widespread genomic surveillance initiatives. This explosion of data calls for innovative methods to enable rapid exploration of the structure of a population based on different data modalities, such as multiple sequence alignments, assemblies and estimates of gene content across different genomes. Here, we present Mandrake, an efficient implementation of a dimensional reduction method tailored for the needs of large-scale population genomics. Mandrake is capable of visualizing population structure from millions of whole genomes, and we illustrate its usefulness with several datasets representing major pathogens. Our method is freely available both as an analysis pipeline (https://github.com/johnlees/mandrake) and as a browser-based interactive application (https://gtonkinhill.github.io/mandrake-web/).This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.Peer reviewe

The dynamics of complex systems. Studies and applications in computer science and biology

Our research has focused on the study of complex dynamics and on their use in both information security and bioinformatics. Our first work has been on chaotic discrete dynamical systems, and links have been established between these dynamics on the one hand, and either random or complex behaviors. Applications on information security are on the pseudorandom numbers generation, hash functions, informationhiding, and on security aspects on wireless sensor networks. On the bioinformatics level, we have applied our studies of complex systems to theevolution of genomes and to protein folding

Audio phylogenetic analysis using geometric transforms

Whenever a multimedia content is shared on the Internet, a mutation process is being operated by multiple users that download, alter and repost a modified version of the original data leading to the diffusion of multiple near-duplicate copies. This effect is also experienced by audio data (e.g., in audio sharing platforms) and requires the design of accurate phylogenetic analysis strategies that permit uncovering the processing history of each copy and identify the original one. This paper proposes a new phylogenetic reconstruction strategy that converts the analyzed audio tracks into spectrogram images and compare them using alignment strategies borrowed from computer vision. With respect to strategies currently-available in literature, the proposed solution proves to be more accurate, does not require any a-priori knowledge about the operated transformations, and requires a significantly-lower amount of computational time

Benchmarking unsupervised near-duplicate image detection

Unsupervised near-duplicate detection has many practical applications ranging from social media analysis and web-scale retrieval, to digital image forensics. It entails running a threshold-limited query on a set of descriptors extracted from the images, with the goal of identifying all possible near-duplicates, while limiting the false positives due to visually similar images. Since the rate of false alarms grows with the dataset size, a very high specificity is thus required, up to 1-10^-9 for realistic use cases; this important requirement, however, is often overlooked in literature. In recent years, descriptors based on deep convolutional neural networks have matched or surpassed traditional feature extraction methods in content-based image retrieval tasks. To the best of our knowledge, ours is the first attempt to establish the performance range of deep learning-based descriptors for unsupervised near-duplicate detection on a range of datasets, encompassing a broad spectrum of near-duplicate definitions. We leverage both established and new benchmarks, such as the Mir-Flick Near-Duplicate (MFND) dataset, in which a known ground truth is provided for all possible pairs over a general, large scale image collection. To compare the specificity of different descriptors, we reduce the problem of unsupervised detection to that of binary classification of near-duplicate vs. not-near-duplicate images. The latter can be conveniently characterized using Receiver Operating Curve (ROC). Our findings in general favor the choice of fine-tuning deep convolutional networks, as opposed to using off-the-shelf features, but differences at high specificity settings depend on the dataset and are often small. The best performance was observed on the MFND benchmark, achieving 96% sensitivity at a false positive rate of 1.43x10^-6

Systematics, functional morphology and distribution of a bivalve (Apachecorbula muriatica gen. et sp. nov.) from the rim of the ‘Valdivia Deep’ brine pool in the Red Sea

The deep brine pools of the Red Sea comprise extreme, inhospitable habitats yet house microbial communities that potentially may fuel adjacent fauna. We here describe a novel bivalve from a deep-sea (1525 m) brine pool in the Red Sea, where conditions of high salinity, lowered pH, partial anoxia and high temperatures are prevalent. Remotely operated vehicle (ROV) footage showed that the bivalves were present in a narrow (20 cm) band along the rim of the brine pool, suggesting that it is not only tolerant of such extreme conditions but is also limited to them. The bivalve is a member of the Corbulidae and named Apachecorbula muriatica gen. et sp. nov. The shell is atypical of the family in being modioliform and thin. The semi-infaunal habit is seen in ROV images and reflected in the anatomy by the lack of siphons. The ctenidia are large and typical of a suspension feeding bivalve, but the absence of guard cilia and the greatly reduced labial palps suggest that it is non-selective as a response to low food availability. It is proposed that the low body mass observed is a consequence of the extreme habitat and low food availability. It is postulated that the observed morphology of Apachecorbula is a result of paedomorphosis driven by the effects of the extreme environment on growth but is in part mitigated by the absence of high predation pressures.We are grateful to all help from the other Leg 4 Red Sea Expedition 2013 KAUST participants; Ioannis Georgakakis, Thor A. Klevjer, Perdana Karim Prihartato, Anders Rostad and Ingrid Solberg. Leonidas Manousakis and Manolis Kalergis from Hellenic Centre for Marine Research (HCMR) assisted in ROV operations. The captain and crew of RV 'Aegaeo' provided support during the entire cruise. Ohoud Mohammed Eid Alharbi assisted with the electron microscopy. The Red Sea Expedition 2013 was sponsored by KAUST. We also thank Ronald Janssen of the Senckenberg Institution for the loan of comparative material from the RV Meteor expeditions

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field