Group-based Robustness: A General Framework for Customized Robustness in the Real World

1 code implementation • 29 Jun 2023 • Weiran Lin, Keane Lucas, Neo Eyal, Lujo Bauer, Michael K. Reiter, Mahmood Sharif

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

no code implementations • 27 Feb 2023 • Keane Lucas, Matthew Jagielski, Florian Tramèr, Lujo Bauer, Nicholas Carlini

It is becoming increasingly imperative to design robust ML defenses.

Adversarial Robustness

RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion

1 code implementation • NeurIPS 2023 • Zhuoqun Huang, Neil G. Marchant, Keane Lucas, Lujo Bauer, Olga Ohrimenko, Benjamin I. P. Rubinstein

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.

Binary Classification Malware Detection

Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination

1 code implementation • 28 Jan 2022 • Keane Lucas, Ross E. Allen

Cooperative artificial intelligence with human or superhuman proficiency in collaborative tasks stands at the frontier of machine learning research.

Decoder

Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks

1 code implementation • 28 Dec 2021 • Weiran Lin, Keane Lucas, Lujo Bauer, Michael K. Reiter, Mahmood Sharif

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

1 code implementation • 19 Dec 2019 • Keane Lucas, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, Saurabh Shintre

Moreover, we found that our attack can fool some commercial anti-viruses, in certain cases with a success rate of 85%.

Feature Engineering Malware Detection +1