Distributed One-class Learning

We propose a cloud-based filter trained to block third parties from uploading privacy-sensitive images of others to online social media. The proposed filter uses Distributed One-Class Learning, which decomposes the cloud-based filter into multiple one-class classifiers. Each one-class classifier captures the properties of a class of privacy-sensitive images with an autoencoder. The multi-class filter is then reconstructed by combining the parameters of the one-class autoencoders. The training takes place on edge devices (e.g. smartphones) and therefore users do not need to upload their private and/or sensitive images to the cloud. A major advantage of the proposed filter over existing distributed learning approaches is that users cannot access, even indirectly, the parameters of other users. Moreover, the filter can cope with the imbalanced and complex distribution of the image content and the independent probability of addition of new users. We evaluate the performance of the proposed distributed filter using the exemplar task of blocking a user from sharing privacy-sensitive images of other users. In particular, we validate the behavior of the proposed multi-class filter with non-privacy-sensitive images, the accuracy when the number of classes increases, and the robustness to attacks when an adversary user has access to privacy-sensitive images of other users

Fault diagnosis method for rolling bearings based on the interval support vector domain description

Aiming at the fault classification problem of the rolling bearing under the uncertain structure parameters work condition, this paper proposes a fault diagnosis method based on the interval support vector domain description (ISVDD). Firstly, intrinsic time scale decomposition is performed for vibration signals of the rolling bearing to get the time-frequency spectrum samples. These samples are divided into a training set and a test set. Then, the training set is used to train the ISVDD. Meanwhile, the dynamic decreasing inertia weight particle swarm optimization is applied to improve the training accuracy of ISVDD model. Finally, the performance of the four interval classifiers is calculated in rolling bearing fault test set. The experimental results show the advantages of the ISVDD model: (1) ISVDD can extend the support vector domain description to solve the uncertain interval rolling bearing fault classification problem effectively; (2) The proposed ISVDD has the highest classification accuracy in four interval classification methods for the different rolling bearing fault types

Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks

In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness

Statistical Degradation Models for Electronics

With increasing presence of electronics in modern systems and in every-day products, their reliability is inextricably dependent on that of their electronics. We develop reliability models for failure-time prediction under small failure-time samples and information on individual degradation history. The development of the model extends the work of Whitmore et al. 1998, to incorporate two new data-structures common to reliability testing. Reliability models traditionally use lifetime information to evaluate the reliability of a device or system. To analyze small failure-time samples within dynamic environments where failure mechanisms are unknown, there is a need for models that make use of auxiliary reliability information. In this thesis we present models suitable for reliability data, where degradation variables are latent and can be tracked by related observable variables we call markers. We provide an engineering justification for our model and develop parametric and predictive inference equations for a data-structure that includes terminal observations of the degradation variable and longitudinal marker measurements. We compare maximum likelihood estimation and prediction results obtained by Whitmore et. al. 1998 and show improvement in inference under small sample sizes. We introduce modeling of variable failure thresholds within the framework of bivariate degradation models and discuss ways of incorporating covariates. In the second part of the thesis we investigate anomaly detection through a Bayesian support vector machine and discuss its place in degradation modeling. We compute posterior class probabilities for time-indexed covariate observations, which we use as measures of degradation. Lastly, we present a multistate model used to model a recurrent event process and failure-times. We compute the expected time to failure using counting process theory and investigate the effect of the event process on the expected failure-time estimates

Designing content-based adversarial perturbations and distributed one-class learning for images.

PhD Theses.This thesis covers two privacy-related problems for images: designing adversarial perturbations that can be added to the input images to protect the private content of images that a user shares with other users from the undesirable automatic inference of classifiers, and training privacy-preserving classifiers on images that are distributed among their owners (image holders) and contain their private information. Adversarial images can be easily detected using denoising algorithms when high-frequency spatial perturbations are used, or can be noticed by humans when perturbations are large and irrelevant to the content of images. In addition to this, adversarial images are not transferable to unseen classifiers as perturbations are small (in terms of the lp norm). In the first part of the thesis, we propose content-based adversarial perturbations that account for the content of the images (objects, colour, structure and details), human perception and the semantics of the class labels to address the above-mentioned limitations of perturbations. Our adversarial colour perturbations selectively modify the colours of objects within chosen ranges that are perceived as natural by humans. In addition to these natural-looking adversarial images, our structure-aware perturbations exploit traditional image processing filters, such as detail enhancement filter and Gamma correction filter, to generate enhanced adversarial images. We validate the proposed perturbations against three classifiers trained on ImageNet. Experiments show that the proposed perturbations are more robust and transferable and cause misclassification with a label that is semantically different from the label of the original image, when compared with seven state-ofthe- art perturbations. Classifiers are often trained by relying on centralised collection and aggregation of images that could lead to significant privacy concerns by disclosing the sensitive information of image holders. In the second part of the thesis, we propose a privacy-preserving technique, called distributed one-class learning, that enables training to take place on edge devices and therefore image holders do not need to centralise their images. Each image holder can independently use their images to locally train a reconstructive adversarial network as their one-class classifier. As sending the model parameters to the service provider would reveal sensitive information, we secret-share the parameters among two non-colluding service providers. Then, we provide cryptographically private prediction services through a mixture of multi-party computation protocols to achieve substantial gains in complexity and speed. A major advantage of the proposed technique is that none of the image holders and service providers can access the parameters and images of other image holders. We quantify the benefits of the proposed technique and compare its 3 4 performance with centralised training on three privacy-sensitive image-based tasks. Experiments show that the proposed technique achieves similar classification performance as non-private centralised training, while not violating the privacy of the image holders

The spatial ecology of an endemic desert shrub

Using spatial patterns to infer biotic and abiotic processes underlying plant population dynamics is an important technique in contemporary ecology, with particular utility when investigating and shrub population dynamics, for which experimental and observational methodologies are rarely feasible. Using a novel one-class classification technique, the locations of over 17,000 Spartocytisus supranubius individuals were mapped from aerial imagery generating a spatially extensive (162 ha), yet accurate, dataset. The recent rapid increase in studies using pattern-process inference has not been accompanied by a rigorous assessment of the behaviour of these techniques, nor an appraisal of their utility in addressing ecological research questions. The first part of the thesis addresses these concerns, investigating whether current methodologies are adequate to test hypotheses concerning spatial interactions. A literature review reveals a preponderance of studies of small, little-replicated plots. The results of the research raise concerns about the utility of spatial point pattern analyses as currently applied in the literature. To avoid inaccurate description of fine-scale spatial structures it is recommended that researchers increase plot replication. Furthermore, studies of spatial structure and population dynamics should account for spatial environmental gradients, whatever plot size is used. The second part of the thesis presents a rigorous investigation, incorporating a priori inference and the application of fine-scale spatial statistical and modelling techniques, of the biotic and abiotic mechanisms underlying the spatial structure and population dynamics of S. supranubius, a leguminous shrub species endemic to the Canary Islands. The spatial structure of S. supranubius populations is consistent with the operation of clonal reproduction and intra-specific competition. However, the results indicate that spatial environmental heterogeneity (from small to broad scales), in particular topography, can interact with biotic processes to generate quantitatively different S. Supranubius patterns in different locations. Future research into the spatial and temporal dynamics of interactions between abiotic and biotic processes is recommended