VOIDFace: a Privacy-Preserving Multi-Network Face Recognition with Enhanced Security

Advancement of machine learning techniques, combined with the availability of large-scale datasets, has significantly improved the accuracy and efficiency of facial recognition. Modern facial recognition systems are trained using large face datasets collected from diverse individuals or public...

PRUNE: a Patching Based Repair Framework for Certifiable Unlearning of Neural Networks

It is often desirable to remove a.k.a. unlearn a specific part of the training data from a trained neural network model. A typical application scenario is to protect the data holder's right to be forgotten, which has been promoted by many recent regulation rules. Existing unlearning methods invol...

Backdoor Attacks against Patch-Based Mixture of Experts

As Deep Neural Networks DNNs continue to require larger amounts of data and computational power, Mixture of Experts MoE models have become a popular choice to reduce computational complexity. This popularity increases the importance of considering the security of MoE architectures. Unfortunately,...

What’s wrong with patch-based Vulnerability Management checks?

My last post about Guinea Pigs and Vulnerability Management products may seem unconvincing without some examples. So, let's review one. It's a common problem that exists among nearly all VM vendors, I will demonstrate it on Tenable Nessus. If you perform vulnerability scans, you most likely seen...