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Found 13 papers, 12 papers with code
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy
EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch, TensorFlow, JAX, and NumPy.
Fast Differentiable Clipping-Aware Normalization and Rescaling
When the rescaled perturbation $\eta \vec{\delta}$ is added to a starting point $\vec{x} \in D$ (where $D$ is the data domain, e. g. $D = [0, 1]^n$), the resulting vector $\vec{v} = \vec{x} + \eta \vec{\delta}$ will in general not be in $D$.
Modeling patterns of smartphone usage and their relationship to cognitive health
The ubiquity of smartphone usage in many people's lives make it a rich source of information about a person's mental and cognitive state.
Accurate, reliable and fast robustness evaluation
We here develop a new set of gradient-based adversarial attacks which (a) are more reliable in the face of gradient-masking than other gradient-based attacks, (b) perform better and are more query efficient than current state-of-the-art gradient-based attacks, (c) can be flexibly adapted to a wide range of adversarial criteria and (d) require virtually no hyperparameter tuning.
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks
Modern neural networks are highly non-robust against adversarial manipulation.
On Evaluating Adversarial Robustness
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult.
Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations.
Adversarial Vision Challenge
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks.
Towards the first adversarially robust neural network model on MNIST
Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans.
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Such decision-based attacks are (1) applicable to real-world black-box models such as autonomous cars, (2) need less knowledge and are easier to apply than transfer-based attacks and (3) are more robust to simple defences than gradient- or score-based attacks.
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models.
Comparing deep neural networks against humans: object recognition when the signal gets weaker
In addition, we find progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
