Search Results for author: Stavros Tripakis

Found 7 papers, 4 papers with code

On Neural Network Equivalence Checking using SMT Solvers

no code implementations22 Mar 2022 Charis Eleftheriadis, Nikolaos Kekatos, Panagiotis Katsaros, Stavros Tripakis

Two pretrained neural networks are deemed equivalent if they yield similar outputs for the same inputs.

Knowledge Distillation

On tolerance of discrete systems with respect to transition perturbations

no code implementations8 Oct 2021 Rômulo Meira-Góes, Eunsuk Kang, Stéphane Lafortune, Stavros Tripakis

In this paper, we propose an approach for analyzing control systems with respect to their tolerance against environmental perturbations.

Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems

1 code implementation5 Oct 2021 Lisa Oakley, Alina Oprea, Stavros Tripakis

We outline a class of threat models under which adversaries can perturb system transitions, constrained by an $\varepsilon$ ball around the original transition probabilities.

Adversarial Robustness

Automated Attacker Synthesis for Distributed Protocols

3 code implementations2 Apr 2020 Max von Hippel, Cole Vick, Stavros Tripakis, Cristina Nita-Rotaru

Distributed protocols should be robust to both benign malfunction (e. g. packet loss or delay) and attacks (e. g. message replay) from internal or external adversaries.

Cryptography and Security Formal Languages and Automata Theory

Metrics and methods for robustness evaluation of neural networks with generative models

1 code implementation4 Mar 2020 Igor Buzhinsky, Arseny Nerinovsky, Stavros Tripakis

In this paper, we propose several metrics to measure robustness of classifiers to natural adversarial examples, and methods to evaluate them.

Adversarial Robustness Image Classification

Automated Synthesis of Secure Platform Mappings

1 code implementation10 May 2017 Eunsuk Kang, Stephane Lafortune, Stavros Tripakis

In this paper, we introduce the problem of synthesizing a property-preserving platform mapping: A set of implementation decisions ensuring that a desired property is preserved from a high-level design into a low-level platform implementation.

Software Engineering

Learning Moore Machines from Input-Output Traces

no code implementations25 May 2016 Georgios Giantamidis, Stavros Tripakis

We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging.

Learning Theory

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