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Found 9 papers, 6 papers with code
Generating Adversarial Examples with Adversarial Networks
A challenge to explore adversarial robustness of neural networks on MNIST.
Proof of Luck: an Efficient Blockchain Consensus Protocol
In the paper, we present designs for multiple blockchain consensus primitives and a novel blockchain system, all based on the use of trusted execution environments (TEEs), such as Intel SGX-enabled CPUs.
Cryptography and Security Distributed, Parallel, and Cluster Computing
Spatially Transformed Adversarial Examples
Perturbations generated through spatial transformation could result in large $\mathcal{L}_p$ distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems.
Exploring the Space of Black-box Attacks on Deep Neural Networks
An iterative variant of our attack achieves close to 100% adversarial success rates for both targeted and untargeted attacks on DNNs.
Decision Boundary Analysis of Adversarial Examples
We find that the boundaries around these adversarial examples do not resemble the boundaries around benign examples.
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong
We ask whether a strong defense can be created by combining multiple (possibly weak) defenses.
Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution
Smart contracts are applications that execute on blockchains.
Cryptography and Security
Practical Black-box Attacks on Deep Neural Networks using Efficient Query Mechanisms
An iterative variant of our attack achieves close to 100% attack success rates for both targeted and untargeted attacks on DNNs.
Characterizing Attacks on Deep Reinforcement Learning
To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.
