31 citations · 71 across the 12 of their papers we have counts for
4 papers · 1 filter
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection
Hao Fu, Akshaj Kumar Veldanda, Prashanth Krishnamurthy +2
This paper proposes a new defense against neural network backdooring attacks that are maliciously trained to mispredict in the presence of attacker-chosen triggers. Our defense is…
On Evaluating Neural Network Backdoor Defenses
Akshaj Veldanda, Siddharth Garg
Deep neural networks (DNNs) demonstrate superior performance in various fields, including scrutiny and security. However, recent studies have shown that DNNs are vulnerable to back…
CryptoNAS: Private Inference on a ReLU Budget
Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen +1
Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to p…
NNoculation: Catching BadNets in the Wild
Akshaj Kumar Veldanda, Kang Liu, Benjamin Tan +5
This paper proposes a novel two-stage defense (NNoculation) against backdoored neural networks (BadNets) that, repairs a BadNet both pre-deployment and online in response to backdo…