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Showing 1 to 20 of 37 for “"Adversarial training"”.
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Multi-Domain Text Classification with Adversarial Training
… domain-dependent task, a subtle shift between training and testing data distributions can cause catastrophic performance deterioration. Moreover, the availability of massive labeled data varies among different domains in real-world applications. Therefore, it is of great importance to …
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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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Understanding Adversarial Training: Improve Image Recognition Accuracy of Convolution Neural Network
… neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence.</p> <p>The main …
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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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Improved pose estimation accuracy of monocular deep visual odometry against dynamic entities via adversarial training
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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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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Adversarial machine learning in computer vision: attacks and defenses on machine learning models
… networks are demonstrated to be vulnerable to adversarial examples at the test time. Adversarial examples are malicious inputs generated from the legitimate inputs by adding small perturbations in order to fool machine learning models to misclassify. We mainly aim to answer two research …
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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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Generative modelling and adversarial learning
… data, such as natural images. Generative adversarial networks (GAN), which are based on the adversarial learning paradigm, are one of the main types of methods for deriving generative models from complicated real-world data. GAN and its variants use a generator to synthesise semantic data …
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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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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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Addressing Deficiencies from Missing Data in Electronic Health Records
… loss in each learning episode. Additionally, an adversarial training procedure is employed to further improve the proposed system, similarly as the conditional generative adversarial networks. Empirical results on the MIMIC-III dataset show that the proposed system achieves superior performance …
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A Minimax Approach for Learning Gaussian Mixtures
Generative adversarial networks (GANs) learn the distribution of observed samples through a zero-sum game between two machine players, a generator and a discriminator. While GANs achieve great success in learning the complex distribution of image, sound, and text data, they perform suboptimally in …
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Deep Generative Models and Biological Applications
… provide an excellent manipulation method for training from rich available unlabeled data set and sampling new data points from underlying high-dimensional probability distributions. </p><p>The recent proposed Variational auto-encoders (VAE) framework is an efficient high-dimensional inference …
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Adversarial methods in machine learning - a federated defense and an attack
… Unfortunately, neural networks are vulnerable to adversarial examples — inputs that are almost indistinguishable from natural data and yet elicit misclassification from the network. The focus of this thesis is to investigate the space of adversarial examples in hitherto novel applications. We …
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An evaluation of the robustness of the natural-adversarial mutual information-based defense and malware classification against adversarial attacks for deep learning
… way. These modified samples are known as adversarial examples and have been crafted with the goal of causing the target DNN to modify its behavior. It has been shown that adversarial examples can be crafted even when the attacker does not have access to the training parameters and model …
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A mixed-method approach to analyze the robustness of natural language processing classifiers
… A large challenge with evaluating these adversarial attacks is the trade-o_ between attack efficiency and text quality. Higher constraints on the attack search space will improve text quality but reduce the attack success rate. In this thesis, I introduce a framework for the evaluation of …
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Generative models and robustness in deep learning for inverse problems
… to introduce an auxiliary network to generate adversarial examples, which is used in a min-max formulation to build robust image reconstruction networks. Theoretically, we show for a linear reconstruction scheme the min-max formulation results in a singular-value(s) filter regularized solution, …
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Optimization and Generalization of Minimax Algorithms
… generalization. In many cases, such as GANs and adversarial training, the objective function for finding the saddle point can be written as an expected value over the data distribution. However, since we often do not have direct access to this distribution, we solve the empirical problem instead, …
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Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures.
… and deparameterized. 3) Resilience Against Adversarial Attacks: Adversarial training with semi-supervised learning enhanced the OCFSDA model's resilience against adversarial attacks. The model was further optimized using quantization techniques. 4) Performance Evaluation: Experimental …
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