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Found 8 papers, 5 papers with code
Group-based Robustness: A General Framework for Customized Robustness in the Real World
In this work, we identify real-world scenarios where the true threat cannot be assessed accurately by existing attacks.
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
It is becoming increasingly imperative to design robust ML defenses.
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
When applied to the popular MalConv malware detection model, our smoothing mechanism RS-Del achieves a certified accuracy of 91% at an edit distance radius of 128 bytes.
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
First, we demonstrate a loss function that explicitly encodes (1) and show that Auto-PGD finds more attacks with it.
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
Moreover, we found that our attack can fool some commercial anti-viruses, in certain cases with a success rate of 85%.
$n$-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers
This paper proposes a new defense called $n$-ML against adversarial examples, i. e., inputs crafted by perturbing benign inputs by small amounts to induce misclassifications by classifiers.
On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples
Combined with prior work, we thus demonstrate that nearness of inputs as measured by $L_p$-norms is neither necessary nor sufficient for perceptual similarity, which has implications for both creating and defending against adversarial examples.
A General Framework for Adversarial Examples with Objectives
Images perturbed subtly to be misclassified by neural networks, called adversarial examples, have emerged as a technically deep challenge and an important concern for several application domains.
