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 20 of 28 for “"Adversarial Robustness"”.
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Adversarial robustness without perturbations
Models resistant to adversarial perturbations are stable around the neighbourhoods of input images, such that small changes, known as adversarial attacks, cannot dramatically change the prediction. Currently, this stability is obtained with Adversarial Training, which directly teaches models to be …
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Understanding Adversarial Robustness in Deep Learning
This thesis studies the adversarial robustness of deep learning models. Our investigation covers various aspects of this phenomenon, including the development of two new defense algorithms, two new attack algorithms, a novel definition of hierarchical adversarial robustness, and an analysis of how …
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Understanding the landscape of adversarial robustness
… models are pervasively vulnerable to adversarial examples. Adversarial examples are inputs that have been slightly perturbed--such that the semantic content is the same--as to cause malicious behavior in a classifier. The study of adversarial robustness has so far largely focused on …
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Enhancing adversarial robustness of deep neural networks
… that have recently been shown to enhance adversarial robustness of machine learning models. In the realm of regularization, Zhang et al. (2019) proposed TRADES, a logit-based regularization optimization function that has been shown to improve upon the robust optimization framework …
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Study on Adversarial Robustness of Phishing Email Detection Models
… public phishing/legitimate datasets are lack adversarial email examples which keeps the detection models vulnerable. To address this problem, we developed an augmented phishing/legitimate email dataset, utilizing different adversarial text attack techniques. In this work, the emails that can …
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Evaluating Adversarial Robustness of Detection-based Defenses against Adversarial Examples
… it has been shown that they are vulnerable to adversarial attacks, a set of techniques that violate the integrity, confidentiality, or availability of such systems. In particular, one of the most studied phenomena concerns adversarial examples, i.e., input samples that are carefully manipulated …
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Improved methodology for evaluating adversarial robustness in deep neural networks
… neural networks are known to be vulnerable to adversarial perturbations, which are often imperceptible to humans but can alter predictions of machine learning systems. Since the exact value of adversarial robustness is difficult to obtain for complex deep neural networks, accuracy of the models …
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Adversarial robustness of deep learning models : an error-correcting codes based approach
… viewpoint. Are modern ML systems robust to adversarial attacks for deployment in critical real-world applications? If not, then how can we make progress in securing these systems against such attacks? In this thesis we first demonstrate the vulnerability of modern ML systems on a real world …
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Investigating the Role of Biological Constraints in Adversarial Robustness via Modeling and Representational Geometry
… humans on various computer vision tasks, the robustness of DNNs to small perturbations is still far from being comparable to the human visual system. Indeed, adversarial attacks, which are very small worst-case perturbations, can reduce the accuracy of state-of-the-art models dramatically to …
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On Passive-Scoping as a method for Large Language Model Robustness to Jailbreaks and Adversarial Examples
… models (LLMs) not only present a challenge for adversarial robustness, but also the natural emergence of unwanted capabilities. Current approaches to safeguarding AI and LLMs predominantly rely on explicitly restricting known instances of these. However, this places a burden on model developers, …
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Adversarial Learned Soups: neural network averaging for joint clean and robust performance
To make computer vision models more adversarially robust, recent literature has made various additions to the adversarial training process, from alternative adversarial losses to data augmentations to the usage of large numbers of diffusion-generated synthetic samples. However, models trained for …
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Aligning AI with Human Values: A Path Towards Trustworthy Machine Learning Systems
… on training-time vulnerabilities, inference-time robustness and alignment, and the long-term impacts of decision-making models. At the training stage, it examines how manipulated training data can compromise vision-language models, facilitating the spread of coherent misinformation. At the …
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Towards machine learning models robust to adversarial examples and backdoor attacks
… two major modes of brittleness of such systems: adversarial examples and backdoor data poisoning attacks. Specifically, in the first part of the thesis, we build a methodology for defending against adversarial examples that is the first one to provide non-trivial adversarial robustness against an …
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On the Security of Speech-based Machine Translation Systems: Vulnerabilities and Attacks
… and accessibility services, the security and robustness of speech-based MT systems remain underexplored. In particular, limited attention has been given to understanding their vulnerabilities under ad- versarial conditions, where malicious actors intentionally craft or manipulate speech inputs …
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Adversarial Resilient and Privacy Preserving Deep learning
… model inversion during the training phase and adversarial evasion attacks during model inference phase, aiming to cause the well-trained model to misbehave randomly or purposefully. This dissertation research addresses these problems with dual focuses: First, it aims to provide a fundamental …
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Defending Against Misuse of Synthetic Media: Characterizing Real-world Challenges and Building Robust Defenses
… wild. In addition, we propose practical low-cost adversarial attacks, and systematically measure the adversarial robustness of existing defenses. Our findings reveal that most defenses show significant degradation in performance under real-world detection scenarios, which leads to the second …
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On exploiting structures for deep learning algorithms with matrix estimation
… following two important learning problems: 1. Adversarial robustness. Deep neural networks are vulnerable to adversarial attacks. This thesis proposes ME-Net, a defense method that leverages ME. In ME-Net, images are preprocessed using two steps: first pixels are randomly dropped from the …
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Efficient Edge Intelligence in the Era of Big Data
… model. Besides, we propose to further introduce adversarial robustness to the student model, by stimulating the student model to correctly identify inputs that have adversarial perturbation. Experiments demonstrate that the knowledge distillation student model has comparable performance to the …
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Towards Deployable Robust Text Classifiers
… use increases, expectations about their level of robustness, fairness, accuracy, and other metrics increase in turn. In this dissertation, we aim to develop more deployable and robust text classifiers, with a focus on improving classifier robustness against adversarial attacks by developing both …
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Robust and Efficient AI-models for Medical Image Reconstruction, Segmentation, and Multimodal Knowledge Distillation
… and remain robust against noise and adversarial perturbations. Overcoming these barriers requires moving beyond narrowly data-driven systems toward AI frameworks that are both technically sophisticated and broadly adaptable to the complexity of clinical practice. This dissertation …
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