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Found 8 papers, 5 papers with code
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
Although guidelines and best practices have been suggested to improve current adversarial robustness evaluations, the lack of automatic testing and debugging tools makes it difficult to apply these recommendations in a systematic manner.
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.
