arXiv research

Interpolating label noise makes models vulnerable to adversarial attacks.

Label noise in adversarial training leads to robust overfitting, explained and mitigated.

Adversarial noises are linearly separable for random neural networks.

Paper tackles robustness in adversarial noise with a meta-optimizer.

Colored noise improves neural network robustness against adversarial attacks.

Proposes a new model for noisy labels considering multiple labelers and adversarial attacks.

Study efficient learning of robust halfspaces with noise.

We present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise r…

New method enhances neural network robustness against adversarial attacks.

Paper presents faster, robust adversarial training methods.

Deep neural networks (DNNs) have been widely used in the fields such as natural language processing, computer vision and image recognition. But several studies have been shown that deep neural networks can be easily fooled by artificial examples with some perturbations, which are widely known as adversarial examples. A…

SGD-trained neural networks generalize well even with adversarial label noise.

Paper shows adversarial training can be fooled by new type of noise.

Convolutional neural networks (CNNs) are known for their good performance and generalization in vision-related tasks and have become state-of-the-art in both application and research-based domains. However, just like other neural network models, they suffer from a susceptibility to noise and adversarial attacks. An adv…

We introduce a new approach for designing computationally efficient learning algorithms that are tolerant to noise, and demonstrate its effectiveness by designing algorithms with improved noise tolerance guarantees for learning linear separators. We consider both the malicious noise model and the adversarial label nois…

Meta framework generates noise to improve multi-attack robustness.

Deep networks can overfit benignly but still be vulnerable to adversarial attacks.

Many state-of-the-art machine learning models such as deep neural networks have recently shown to be vulnerable to adversarial perturbations, especially in classification tasks. Motivated by adversarial machine learning, in this paper we investigate the robustness of sparse regression models with strongly correlated co…

Adversarial training improves robustness of halfspaces in noisy data.

We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modeling of random noise with the same loss function used during posterior maximization, improves a model's understanding of the data manifold …

The paper examines how adversarial training and noise affect neural network performance.

Polynomial-time tester-learner for general halfspaces with Gaussian adversarial noise.

Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large datasets. The discovery of a number of well-known attacks such as dataset poisoning, adversar…

Adversarial training makes logistic regression weight loss landscapes sharper.

Generative adversarial networks (GANs) are neural networks that learn data distributions through adversarial training. In intensive studies, recent GANs have shown promising results for reproducing training images. However, in spite of noise, they reproduce images with fidelity. As an alternative, we propose a novel fa…

Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained…

Develops GLRT for defending against adversarial attacks in hypothesis testing.

Polynomial-time algorithm for learning halfspaces with Gaussian-distributed data and adversarial noise.

Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polic…

We consider the non-parametric regression problem under Huber's $ε$-contamination model, in which an $ε$ fraction of observations are subject to arbitrary adversarial noise. We first show that a simple local binning median step can effectively remove the adversary noise and this median estimator is minimax optimal up t…

Efficiently learns a single neuron with adversarial noise, improving on prior work.

Non-convex SGD learns halfspaces with adversarial label noise efficiently.

Robust RL improves controller robustness to dynamics variations using adversarial populations.

Neural Ordinary Differential Equation (Neural ODE) has been proposed as a continuous approximation to the ResNet architecture. Some commonly used regularization mechanisms in discrete neural networks (e.g. dropout, Gaussian noise) are missing in current Neural ODE networks. In this paper, we propose a new continuous ne…

Proposes MGPLL for PL learning with non-random noise.

Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer across models. We hypothesize that adversarial weakness is composed of three sources of bias: architecture, dataset, and random initialization.…

Study improves robustness and sparsity in linear regression with adversarial outliers and heavy-tailed noise.

The use of unsupervised data in addition to supervised data in training discriminative neural networks has improved the performance of this clas- sification scheme. However, the best results were achieved with a training process that is divided in two parts: first an unsupervised pre-training step is done for initializ…

New algorithm learns halfspaces with adversarial noise efficiently.

Quantum classification robustness improved via quantum hypothesis testing.

Robustly estimates linear regression coefficients with adversarial and noisy data.

Randomized smoothing reduces accuracy in ML models, especially at higher noise levels.

Image classifiers often suffer from adversarial examples, which are generated by strategically adding a small amount of noise to input images to trick classifiers into misclassification. Over the years, many defense mechanisms have been proposed, and different researchers have made seemingly contradictory claims on the…

Unified framework improves neural network robustness against label noise and adversarial attacks.

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by adding slight perturbation…

Deep neural networks (DNNs) have achieved excellent performance on several tasks and have been widely applied in both academia and industry. However, DNNs are vulnerable to adversarial machine learning attacks, in which noise is added to the input to change the network output. We have devised an image-processing-based …

Paper provides optimal statistical guarantees for adversarial robustness in Gaussian classification.

Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to small modifications of a correctly handled input. Less surprisingly, image…