Improving Adversarial Attacks on Decision Tree Ensembles: Exploring the impact of starting points on attack performance

Most of the adversarial attacks suitable for attacking decision tree ensembles work by doing multiple local searches from randomly selected starting points, around the to be attacked victim. In this thesis we investigate the impact of these starting points on the performance of the attack, and find that the starting points significantly impact...

Adversarial Attacks against the Perception System of Autonomous Vehicles

The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety. These systems are of paramount importance due to their direct impact on the lives of passengers and pedestrians....

Adversarial Attack and Training on Deep Learning-based Gaze estimation

Recently, while gaze estimation has gained a substantial improvement by using deep learning models, research had shown that neural networks are weak against adversarial attacks. Despite researchers has been done numerous on adversarial training, there are little to no studies on adversarial training in gaze estimation. Therefore, the objective...

Are BERT-based fact-checking models robust against adversarial attack?

We seek to examine the vulnerability of BERT-based fact-checking. We implement a gradient based, adversarial attack strategy, based on Hotflip swapping individual tokens from the input. We use this on a pre-trained ExPred model for fact-checking. We find that gradient based adversarial attacks are ineffective against ExPred. Uncertainties about...

Champagne Taste on a Beer Budget: Better Budget Utilisation in Multi-label Adversarial Attacks

Abstract—Multi-label classification is an important branch of classification problems as in many real world classification scenarios an object can belong to multiple classes simultaneously. Deep learning based classifiers perform well at image classifica- tion but their predictions have shown to be unstable when subject to small input...

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...

Using object-specific frequency information from labeled data to improve a CNN’s robustness to adversarial attacks

Convolutional Neural Networks are particularly vulnerable to attacks that manipulate their data, which are usually called adversarial attacks. In this paper, a method of filtering images using the Fast Fourier Transform is explored, along with its potential to be used as a defense mechanism to such attacks. The main contribution that differs...

Top-Down Networks: A coarse-to-fine reimagination of CNNs

Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones...