1 code implementation • 27 May 2020 • Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, Martin Vechev
We present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and nonlinear recurrent update functions by combining sampling, optimization, and Fermat's theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron.
no code implementations • 19 May 2020 • Martin Kotuliak, Sandro E. Schoenborn, Andrei Dan
Our key idea is to generate adversarial objects that are unrelated to the classes identified by the target object detector.
3 code implementations • 4 Jun 2018 • Petar Tsankov, Andrei Dan, Dana Drachsler Cohen, Arthur Gervais, Florian Buenzli, Martin Vechev
To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property.
Cryptography and Security