Outlier detection is an essential part of modern systems. It is used to detect anomalies in behaviour or performance of systems or subjects, such as fall detection in smartwatches or voltage irregularity detection in batteries. This provides early indications of something of potential problems. A part of outlier detection that is not often analysed is the performance of algorithms in environments with data from only one subject, versus environments with data from multiple subjects. This paper aims to answer the questions regarding the performance of Gaussian Mixture Models (GMM) and DBSCAN in these different environments. This paper focuses on time series data collected from consumer-grade wearables like smartwatches. In this paper, the outliers are defined manually, as the used data set did not contain predefined outliers. This research considers both outliers defined within the subject data, and the use of other subjects as outliers. Results from this paper indicate that the amount of subjects in the environment is not the sole factor in the performance of these algorithms. Rather, it is a combination of the amount of subjects in the environment and the type of outlier to be detected. Results show that a GMM has difficulty distinguishing subjects that are similar when using another subject as outlier data. On average, DBSCAN outperforms a GMM in almost all cases, and DBSCAN is a lot more consistent in its performance than a GMM.

Gaze estimation is an important area of research used in a wide range of applications. However, existing models trained for gaze estimation often suffer from high computational costs. In this study, frequency domain channel selection techniques were explored to decrease these costs by reducing the size of the input data. The main research objective was to investigate the impact of channel selection on the latency and accuracy of frequency domain gaze estimation. Channel selection methods used in related research were adapted and applied to the domain of gaze estimation. The evaluation was conducted on two popular network architectures used in this field, namely the AlexNet and ResNet-18. Multiple channel selection models were designed for each architecture and compared to a traditional RGB approach with the same network structure. Experimental results showed significant speedups during training, calibration, and inference with marginal accuracy loss. The specific speedups that the top-performing models of both the architectures achieves were 3.3, 4.0, and 1.35 for the AlexNet, and 1.5, 1.7, and 1.35 for the ResNet-18. Accompanying these speedups the AlexNet model error only increased by 0.08 degrees compared to a traditional RGB approach, while the ResNet-18 model lost around 0.44 degrees. All the code used in this research is publicly available on GitHub (https://github.com/tpenning/DLFDFaceGazeEstimation).

Advancements in the precision and accuracy of consumer-grade wearables, such as a Fitbit, have enabled the identification and therefore authentication of individuals based on their emitted heart frequencies using these wrist-worn devices. With this type of authentication, a password is essentially sent out every second. This makes it a perfect form of authentication in fields where constant authentication is crucial. However, not much is known about how different types of cardiac diagnosis (e.g. fit or obese) influence the accuracy of this type of authentication. In this paper, it will be shown how different types of cardiac diagnosis affect the accuracy of Deep Neural Networks to identify individuals by heart rate. This study is done with data obtained from 14 subjects, having different types of cardiac diagnosis. A deep neural network consisting of multiple convolutional layers is being used to conduct the experiments. It has been shown that subjects with a paroxysmal atrial fibrillation diagnosis improve the accuracy the most, compared to the reference (normal healthy) subjects. On the other hand, (very) fit subjects decrease the accuracy the most. Heart failure and obese subjects have a similar accuracy compared to reference subjects.

Emotion recognition in Virtual Reality(VR) has the potential to offer numerous benefits across various sectors such as mental healthcare, education, marketing, entertainment, etc. Although emotion recognition itself is a mature field, the sub-field of VR-based emotion recognition is still in its early stages of development. It was found that a limiting factor in the progress of this field is a lack of sufficient data for research and development of advanced deep learning models. Also, the equipment currently used to measure emotion-related signals is expensive and impractical for general usage. This thesis aims to support the progress in this field by creating a VR-based emotion recognition dataset using VR equipment only. This addresses the problem of insufficient data available for research and development, and also reduces the reliance on expensive and impractical equipment for emotion recognition. To create a good quality dataset, several important things had to be addressed. First of all, the stimuli to evoke the emotions had to be carefully selected to ensure that genuine emotional responses were evoked and recorded in the dataset. Then, an efficient data collection system had to be created to ensure that the data collection process ran effectively, smoothly and consistently. Then, a proper labeling process had to be designed to annotate the data as accurately as possible. Finally, the compiled dataset was validated by showing that the chosen stimuli were effective in evoking the intended emotions. This was verified through the analysis of pupil response data, which is one of the recorded data modalities.

This research investigates backdoor attacks on deep regression models, focusing on the gaze estimation task. Backdoor triggers can be used to poison a model during training phase to have a hidden misbehaving functionality. For gaze estimation, a backdoored model will return an attacker-chosen target gaze direction, normally incorrect, regardless of image content, when presented with an image containing a trigger. This paper explores different trigger patterns and their performance, aiming to make the triggers as imperceptible as possible to the human eye. Furthermore, the research explores a method to make the corruption of the training set as stealthy as possible while achieving a good attack performance. In the end, the findings showed that backdoor attacks on deep regression models can be made imperceptible and highly performant using complex trigger patterns. While stealthy corruption was also possible, achieving an efficient model would require a larger dataset.

This research investigates backdoor attacks on deep regression models, focusing on the gaze estimation task. Backdoor triggers can be used to poison a model during training phase to have a hidden misbehaving functionality. For gaze estimation, a backdoored model will return an attacker-chosen target gaze direction, normally incorrect, regardless of image content, when presented with an image containing a trigger. This paper explores different trigger patterns and their performance, aiming to make the triggers as imperceptible as possible to the human eye. Furthermore, the research explores a method to make the corruption of the training set as stealthy as possible while achieving a good attack performance. In the end, the findings showed that backdoor attacks on deep regression models can be made imperceptible and highly performant using complex trigger patterns. While stealthy corruption was also possible, achieving an efficient model would require a larger dataset.

This research investigates backdoor attacks on deep regression models, focusing on the gaze estimation task. Backdoor triggers can be used to poison a model during training phase to have a hidden misbehaving functionality. For gaze estimation, a backdoored model will return an attacker-chosen target gaze direction, normally incorrect, regardless of image content, when presented with an image containing a trigger. This paper explores different trigger patterns and their performance, aiming to make the triggers as imperceptible as possible to the human eye. Furthermore, the research explores a method to make the corruption of the training set as stealthy as possible while achieving a good attack performance. In the end, the findings showed that backdoor attacks on deep regression models can be made imperceptible and highly performant using complex trigger patterns. While stealthy corruption was also possible, achieving an efficient model would require a larger dataset.

With the rise of deep learning and the widespread use of deep neural networks, backdoor attacks have become a significant security threat, drawing considerable research interest. One such attack is the SIG backdoor attack, which introduces signals to the images. We look into three types of SIG backdoor attacks - ramp, triangle, and sinusoidal signals. Most of the works in the field of AI security, however, have focused on deep classification tasks, leaving deep regression tasks unexplored. In this study, we adapt the SIG backdoor attack for use in a deep regression model (DRM) used to estimate head pose. Our objective is to create a backdoor attack that remains imperceptible to the human eye while being detectable by the DRM. To evaluate the effectiveness of our attack, we employ two approaches: average angular error and accuracy in a discretized continuous space. Additionally, we adapt fine-tuning as a countermeasure against the backdoor attack. By implementing this strategy, we aim to reduce the risk of backdoor attacks and improve the robustness of deep regression models in head pose estimation.

Deep Regression Models (DRMs) are a subset of deep learning models that output continuous values. Due to their performance, DRMs are widely used as critical components in various systems. As training a DRM is resource-intensive, many rely on pre-trained third-party models, which can leave a worrying amount of systems vulenerable to backdoor attacks. A backdoored model is an otherwise legitimate model that maliciously changes its behaviour whenever a predetermined backdoor trigger is present. While numerous works on backdoor attacks on deep learning models focus on classification problems, very little work has focused on DRMs. We formulate and evaluate a backdoor attack on a DRM using WaNet, a method that relies on warping-based triggers that are difficult to detect by both human and machine defence methods. We successfully train a backdoored (poisoned) DRM with the backdoor working for both grayscale and coloured inputs. Further experiments show that the malicious backdoor behaviour can be subdued by fine-tuning the poisoned model.

Event cameras are cameras that capture events asynchronously based on changes in lighting. They offer multiple benifits, but pose challenges in computer vision due to their asynchronous nature and hard to capture ground truth values to compare against. This paper shows the effects training of a state of the art unsupervised learning algorithm Taming Contrast Maximisation for predicting optical flow on a new dataset BlinkFlow which promises improvements in performance of supervised algorithms. This paper aims to see if these improved performances also happen for unsupervised models. Results of this research were inconclusive for the effectiveness of training unsupervised models, but it was shown that pretrained models on DSEC and MVSEC datasets did not perform well on this new dataset.

Event cameras are novel sensors whose high temporal resolution and bandwidth motivate their use for the optical flow estimation problem. However, the properties of event cameras also introduce a vulnerability to flickering. Flickering hurts the perceptibility of motion by overwhelming event data with unrelated information. The single existing event de-flicker method (EFR) is built for scenarios where the relative position of the camera and the flickering object is constant, which is uncommon in motion-heavy optical flow estimation scenarios. Our contribution is a new de-flickering method that incorporates spatial awareness of nearby pixels. We hypothesize this feature to increase robustness to movement, and thus to better improve optical flow accuracy. Compared to EFR our method falters at filtering intensely flickering surfaces, but better preserves the spatial coherence of edges. However, we observe that both de-flickering methods remove much geometric information, especially given slow motion or weak ambient illumination. Our benchmarking shows that neither our method nor EFR significantly affects optical flow estimation accuracy, despite reducing event counts by 50-65%. Overall, we conclude that the niche benefits of spatial filtering are nullified by the result that filtering hardly affects optical flow estimation.

Event cameras are novel sensors whose high temporal resolution and bandwidth motivate their use for the optical flow estimation problem. However, the properties of event cameras also introduce a vulnerability to flickering. Flickering hurts the perceptibility of motion by overwhelming event data with unrelated information. The single existing event de-flicker method (EFR) is built for scenarios where the relative position of the camera and the flickering object is constant, which is uncommon in motion-heavy optical flow estimation scenarios. Our contribution is a new de-flickering method that incorporates spatial awareness of nearby pixels. We hypothesize this feature to increase robustness to movement, and thus to better improve optical flow accuracy. Compared to EFR our method falters at filtering intensely flickering surfaces, but better preserves the spatial coherence of edges. However, we observe that both de-flickering methods remove much geometric information, especially given slow motion or weak ambient illumination. Our benchmarking shows that neither our method nor EFR significantly affects optical flow estimation accuracy, despite reducing event counts by 50-65%. Overall, we conclude that the niche benefits of spatial filtering are nullified by the result that filtering hardly affects optical flow estimation.

This study aims to provide insights in applying different data augmentation techniques to the input data of a convolutional neural network that estimates gaze. Gaze is used in numerous research domains for understanding and predicting emotions and actions from humans. Data augmentations consists of techniques to increase the size, variance and quality of training data to create better deep-learning models. Data augmentation is a widely used technique to reduce overfitting and increase accuracy of deep learning models. This research combines those two fields by first applying different individual data augmentations on the task of gaze estimation and after that combining the most useful methods to decrease the mean angular error even further. The results show that small geometric transformations, such as translating the image a portion of 15% or flipping the image horizontally 50% of the time give the most significant reductions in mean angular error. For individually applied data augmentation methods flipping got the best improvement, with 33% and 35% for both models in comparison to the baseline model. The best result is obtained by combining flipping with translation which got a mean angular error of 1.396 and 1.389 for both models. For obtaining the results a lot of training is necessary, which was the main limitation to conduct the experiments.

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 dataset, the more queries required to gain an accurate substitute model. Through each state-of-the-art model extraction algorithm, one thing that is not thoroughly optimised are the hyperparameters of the models, and optimizing them has been found to have a strong impact on accuracy of the substitute model. To attempt to reduce the number of queries required, research has been done to find the effects of optimizing hyperparameters for both MNIST and fashionMNIST datasets. This is done through grid search and random search. The results show that proper hyperparameter tuning can reduce the number of queries required to perform model stealing if they are not already optimized. Examples include requiring 125000 + queries to achieve 95% accuracy for the MNIST dataset with some hyperparameter combinations to only requiring 15000.