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Found 3 papers, 2 papers with code
Detecting AI Trojans Using Meta Neural Analysis
To train the meta-model without knowledge of the attack strategy, we introduce a technique called jumbo learning that samples a set of Trojaned models following a general distribution.
Differentially Private Data Generative Models
We conjecture that the key to defend against the model inversion and GAN-based attacks is not due to differential privacy but the perturbation of training data.
SWEET: Serving the Web by Exploiting Email Tunnels
As the operation of SWEET is not bound to specific email providers we argue that a censor will need to block all email communications in order to disrupt SWEET, which is infeasible as email constitutes an important part of today's Internet.
Cryptography and Security
