Deep learning for Plankton and Coral Classification

Oceans are the essential lifeblood of the Earth: they provide over 70% of the oxygen and over 97% of the water. Plankton and corals are two of the most fundamental components of ocean ecosystems, the former due to their function at many levels of the oceans food chain, the latter because they provide spawning and nursery grounds to many fish populations. Studying and monitoring plankton distribution and coral reefs is vital for environment protection. In the last years there has been a massive proliferation of digital imagery for the monitoring of underwater ecosystems and much research is concentrated on the automated recognition of plankton and corals. In this paper, we present a study about an automated system for monitoring of underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different CNN models, fine tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning pretrained CNN for underwater imagery analysis, the opportunity of using different datasets for pretraining models, the possibility to design an ensemble using the same architecture with small variations in the training procedure. The experimental results are very encouraging, our experiments performed on 5 well-knowns datasets (3 plankton and 2 coral datasets) show that the fusion of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report performance of both single CNN and ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model

Efficient Unsupervised Learning for Plankton Images

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amount of plankton image data. The adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation, due to both the huge quantity of acquired data and the numerosity of plankton species. To address these challenges, we propose an efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms. We build a set of image descriptors exploiting a two-step procedure. First, a Variational Autoencoder (VAE) is trained on features extracted by a pre-trained neural network. We then use the learnt latent space as image descriptor for clustering. We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images. The proposed pipeline outperforms the benchmark algorithms for all the plankton datasets included in our analysis, providing better image embedding properties

The Cooperative Participatory Evaluation of Renewable Technologies on Ecosystem Services (CORPORATES)

Publisher PD

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers

Malware classifiers are subject to training-time exploitation due to the need to regularly retrain using samples collected from the wild. Recent work has demonstrated the feasibility of backdoor attacks against malware classifiers, and yet the stealthiness of such attacks is not well understood. In this paper, we focus on Android malware classifiers and investigate backdoor attacks under the clean-label setting (i.e., attackers do not have complete control over the training process or the labeling of poisoned data). Empirically, we show that existing backdoor attacks against malware classifiers are still detectable by recent defenses such as MNTD. To improve stealthiness, we propose a new attack, Jigsaw Puzzle (JP), based on the key observation that malware authors have little to no incentive to protect any other authors' malware but their own. As such, Jigsaw Puzzle learns a trigger to complement the latent patterns of the malware author's samples, and activates the backdoor only when the trigger and the latent pattern are pieced together in a sample. We further focus on realizable triggers in the problem space (e.g., software code) using bytecode gadgets broadly harvested from benign software. Our evaluation confirms that Jigsaw Puzzle is effective as a backdoor, remains stealthy against state-of-the-art defenses, and is a threat in realistic settings that depart from reasoning about feature-space-only attacks. We conclude by exploring promising approaches to improve backdoor defenses

Automatic system for the detection and recognition of phytoplankton in digital microscope imaging

[Abstract] The quality of water can be compromised by the proliferation of toxic species of phytoplankton. When these blooms occur in rivers and reservoirs used for the water supply, this event can have a negative impact on human health. Currently, to determine the existence of risk, experts rudimentarily monitor phytoplankton populations by sampling and analysing the water. This analysis consits on the identification of dangerous species and its biologic volume. All in all, this process is long and tedious when the amount of samples that need to be analysed in order to obtain a quality and representative measure is taken into account, which also needs to be carried out periodically for each water source. The taxonomic process requires broad experience and training for the personnel involved. The automation of these tasks is highly desirable as it would free the experts from part of the work at the same time as that eliminates subjective factors that may impact in the overall quality of the process. In this work the intention is to help experts starting from images obtained directly from a conventional microscope, differentiating it from other similar works in the state of the art that use specific hardware. Computer vision techniques will be used to detect candidate individuals and artificial intelligence methods to recognise relevant phytoplankton species, that is, the toxic ones, distinguishing them from the rest of objects in the images like, for example, inorganic materials. Finally, the phytoplankton organisms will be classified to obtain a metric that counts the dangerous ones and so be able to analyse the quality of the water.[Resumo] A calidade da auga pode verse ameazada pola proliferación de especies tóxicas de fitoplancto. Cando estas proliferacións ocorren en ríos e encoros utilizados na subministración de auga potable este feito pode ter impactos negativos na saúde humana. Actualmente, para determinar a existencia de risco, os expertos monitorizan, de forma rudimentaria, as poboacións de fitoplancto mediante a recolección de mostras e a súa correspondente análise. Esta análise consiste na identificación das especies perigosas e o rexistro do seu volume biolóxico. Con todo, este proceso resulta longo e tedioso se se ten en conta a cantidade de mostras a analizar para poder ofrecer unhas métricas fiables e representativas, as cales se deben realizar periodicamente para cada unha das fontes de auga destinadas ao consumo. Así mesmo, o proceso taxonómico require unha ampla experiencia e formación específica do persoal involucrado. A automatización destas tarefas é moi desexable xa que libera aos expertos de parte do traballo, á vez que evita factores subxectivos que poidan influír na calidade global do proceso. Neste traballo preténdese axudar aos expertos partindo de imaxes obtidas directamente do microscopioconvencional, diferenciándoo de traballos similares do estado do arte que requiren hardware específico. Empregaranse técnicas de procesado de imaxe e visión artificial para detectar individuos candidatos e técnicas de intelixencia artificial para recoñecer as especies de fitoplancto relevantes, é dicir, as tóxicas, distinguíndoas do resto de obxectos nas imaxes, como, por exemplo, materiais inertes ou inorgánicos. Por último, os microogranismos de fitoplancto son clasificados para obter unha métrica que contabilice os perigosos e poder, así, analizar a calidade da auga.Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2018/201

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa