Performance analysis of an offline digital Euro prototype

Recent years have seen an increasing interest in stablecoins from major corporate and governmental parties. The European Central Bank is investigating the possibility of introducing its own Central Bank Digital Currency. The desired features of such a currency are under discussion. One such feature is offline spending: the ability to use the...

A Human-In-the-Loop Framework to Assess Multimodal Machine Learning Models

Recent works explain the DNN models that perform image classification tasks following the "attribution, human-in-the-loop, extraction" workflow. However, little work has looked into such an approach for explaining DNN models for language or multimodal tasks. To address this gap, we propose a framework that explains and assesses the model...

Decreasing Model Stealing Querying for Black Box Adversarial Attacks

A machine learning classifier can be tricked us- ing adversarial attacks, attacks that alter images slightly to make the target model misclassify the image. To create adversarial attacks on black-box classifiers, a substitute model can be created us- ing model stealing. The research question this re- port address is the topic of using model...

Black-box Adversarial Attacks using Substitute models: Effects of Data Distributions on Sample Transferability

Machine Learning (ML) models are vulnerable to adversarial samples — human imperceptible changes to regular input to elicit wrong output on a given model. Plenty of adversarial attacks assume an attacker has access to the underlying model or access to the data used to train the model. Instead, in this paper we focus on the effects the data...

A deep dive into the robustness of AdaBoost Ensembling combined with Adversarial Training

Adversarial training and its variants have become the standard defense against adversarial attacks - perturbed inputs designed to fool the model. Boosting techniques such as Adaboost have been successful for binary classification problems, however, there is limited research in the application of them for providing adversarial robustness. In this...

Effect of parameter tuning on reducing the number of queries required to perform model stealing

Model extraction attacks are attacks which generate a substitute model of a targeted victim neural network. It is possible to perform these attacks without a preexisting dataset, but doing so requires a very high number of queries to be sent to the victim model. This is otfen in the realm of several million queries. The more difficult the...

An Empirical Look at Gradient-based Black-box Adversarial Attacks on Deep Neural Networks Using One-point Residual Estimates

In recent years, there has been a great deal of studies about the optimisation of generating adversarial examples for Deep Neural Networks (DNNs) in a black-box environment. The use of gradient-based techniques to get the adversarial images in a minimal amount of input-output correspondence with the attacked model has been extensively studied....

Building Interactive Text-to-SQL Systems

Natural Language Interfaces for Databases (NLIDBs) offer a way for users to reason about data. It does not require the user to know the data structure, its relations, or familiarity with a query language like SQL. It only requires the use of Natural Language. This thesis focuses on a subset of NLIDBs, namely those with 'plain English' sentences...