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Found 23 papers, 11 papers with code
Evaluating the Evaluators: Trust in Adversarial Robustness Tests
Despite significant progress in designing powerful adversarial evasion attacks for robustness verification, the evaluation of these methods often remains inconsistent and unreliable.
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
We present RAID (Robust evaluation of AI-generated image Detectors), a dataset of 72k diverse and highly transferable adversarial examples.
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection
In this paper, we present an in-depth analysis of post-hoc OOD detection methods and investigate their potential to eliminate the need for a memory buffer.
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness
Recent work has proposed neural network pruning techniques to reduce the size of a network while preserving robustness against adversarial examples, i. e., well-crafted inputs inducing a misclassification.
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks
Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models.
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Relevant literature has claimed contradictory remarks in support of and against the robustness of over-parameterized networks.
AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
While novel attacks are continuously proposed, each is shown to outperform its predecessors using different experimental setups, hyperparameter settings, and number of forward and backward calls to the target models.
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
In this work, we show that this problem also affects robustness to adversarial examples, hindering the development of secure model update practices.
$σ$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging.
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging.
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks
Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks for higher sparsity.
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors
Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from adversarial manipulations of the HTML code of the input webpage.
Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training
Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference.
A Survey on Reinforcement Learning Security with Application to Autonomous Driving
Reinforcement learning allows machines to learn from their own experience.
Explaining Machine Learning DGA Detectors from DNS Traffic Data
One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of infected devices (botnet) gets exploited to flood the computational capacity of services through the commands of an attacker.
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
We showcase the usefulness of this dataset by testing the effectiveness of the computed patches against 127 models.
Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes
We find that $71\%$ of the adversarial examples that achieve model-to-model adversarial transferability are misclassified into one of the top-5 classes predicted for the underlying source images.
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference
Adversarial reprogramming allows repurposing a machine-learning model to perform a different task.
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Evaluating robustness of machine-learning models to adversarial examples is a challenging problem.
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified.
Detecting Anomalies from Video-Sequences: a Novel Descriptor
We present a novel descriptor for crowd behavior analysis and anomaly detection.
secml: A Python Library for Secure and Explainable Machine Learning
We present \texttt{secml}, an open-source Python library for secure and explainable machine learning.
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model.
