Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"Adversarial samples"”.
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Design and evaluation of GAN-based models for adversarial training robustness in deep learning
Adversarial attacks show one of the generalization issues of current deep learning models on special distribution shifted data. The adversarial samples generated by the attack algorithm can introduce malicious behavior to any deep learning system that affects the consistency of the deep learning …
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Treadmill Assisted Circumvention of Wearable Sensors-based Gait Authentication
… Our experimental findings suggest that adversarial samples generated with a treadmill's assistance can help circumvent wearable sensors-based gait authentication systems, necessitating reconsideration of their use in high-security environments. In the end, we discuss several possible …
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Adversarial training objectives for generative attacks on text classifiers
In natural language processing, creating textual adversarial examples is challenging. These examples aim to deceive text classifiers into incorrect predictions while maintaining linguistic similarity to genuine inputs. This complexity arises from the need to preserve the original text's fluency, …
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Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring. [Thesis]
… we further exploit it in the context of adversarial attack resistance. The resulting DNN is more resistant to adversarial samples than its benchmark counterparts and other conventional ML methods. To evaluate the effectiveness of our proposal, we considered on-device learning in federated …
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Engineering-driven Machine Learning Methods for System Intelligence
… data points and exploiting limited training samples to make accurate predictions and conduct uncertainty quantification. Third, a Wasserstein-based out-of-distribution detection (WOOD) framework is proposed to strengthen the DNN-based classifier with the ability to detect adversarial samples. …
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Augmenting Multi-modal Question Answering Systems with Retrieval Methods
… 2.0 introduces semi-automatically annotated adversarial samples to address data distribution imbalances and enhance system robustness, showcasing substantial improvements in handling challenging scenarios. Thirdly, the development of FLMR (Fine-grained Late-interaction Multi-modal Retriever), …
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Robust Anomaly Detection in Critical Infrastructure
… However, ML methods are vulnerable to both adversarial and non-adversarial input perturbations. Adversarial perturbations are imperceptible noises added to the input data by an attacker to evade the classification mechanism. Non-adversarial perturbations can be a normal behaviour evolution …
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Secure Machine Learning Based RF Signal Classification for Wireless Systems
… RF signal based on the in-phase/quadrature (I/Q) samples without decoding them. Our research starts with DNN designs in the context of spectrum sharing, focusing on Wi-Fi, LTE-LAA, and 5G NR-U systems that coexist over the unlicensed 5 GHz bands. First, we consider recurrent neural network (RNN) …