208 citations · 588 across the 22 of their papers we have counts for
6 papers · 1 filter
Towards transformation-resilient provenance detection of digital media
Jamie Hayes, Krishnamurthy, Dvijotham +4
Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are difficult to distinguish from natural signals, creating opportu…
Adaptive Webpage Fingerprinting from TLS Traces
Vasilios Mavroudis, Jamie Hayes
In webpage fingerprinting, an on-path adversary infers the specific webpage loaded by a victim user by analysing the patterns in the encrypted TLS traffic exchanged between the use…
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning
Mohammad Naseri, Jamie Hayes, Emiliano De Cristofaro
Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local while only exchanging model updates. Alas, thi…
Extensions and limitations of randomized smoothing for robustness guarantees
Jamie Hayes
Randomized smoothing, a method to certify a classifier's decision on an input is invariant under adversarial noise, offers attractive advantages over other certification methods. I…
Unique properties of adversarially trained linear classifiers on Gaussian data
Jamie Hayes
Machine learning models are vulnerable to adversarial perturbations, that when added to an input, can cause high confidence misclassifications. The adversarial learning research co…
Trade-offs between membership privacy & adversarially robust learning
Jamie Hayes
Historically, machine learning methods have not been designed with security in mind. In turn, this has given rise to adversarial examples, carefully perturbed input samples aimed t…