Muhammad Zaid Hameed
Zaid joined the group as a Research Associate in May 2020. His activities focus on federated learning and adversarial machine learning.
Updates
Research Assistant / Research Associate in Federated and Adversarial Machine Learning
Research Assistant salary in the range: £35,477 to £38,566 per annum*
Research Associate salary in the range: £40,215 to £47,579 per annum
Full Time, Fixed Term appointment for to start ASAP until the 31/11/2021
The Resilient Information Systems Security Group (RISS) in the Department of Computing at Imperial College London is seeking a Research Assistant/Associate to work on EU funded Musketeer project. Musketeer aims to create a federated and privacy preserving machine learning data platform, that is interoperable, efficient and robust against internal and external threats. Led by IBM the project involves 11 academic and industrial partners from 7 countries and will validate its findings in two industrial scenarios in smart manufacturing and health care. Further details about the project can be found at: www.musketeer.eu
Follow this link for further details and how to apply.
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks (CCS ’19)
Our paper on procedural noise adversarial examples has been accepted to the 26th ACM Conference on Computer and Communications Security (ACM CCS ’19).
official: https://dl.acm.org/citation.cfm?id=3345660
code: https://github.com/kenny-co/procedural-advml
Abstract: Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples—perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO v3, with single noise patterns able to fool a model on up to 90% of the dataset. Procedural noise allows us to generate a distribution of UAPs with high universal evasion rates using only a few parameters. Additionally, we propose Bayesian optimization to efficiently learn procedural noise parameters to construct inexpensive untargeted black-box attacks. We demonstrate that it can achieve an average of less than 10 queries per successful attack, a 100-fold improvement on existing methods. We further motivate the use of input-agnostic defences to increase the stability of models to adversarial perturbations. The universality of our attacks suggests that DCN models may be sensitive to aggregations of low-level class-agnostic features. These findings give insight on the nature of some universal adversarial perturbations and how they could be generated in other applications.
Code Release: “Towards Poisoning of Deep Learning Algorithms with Back-gradient optimization”
We have released the code with a demo or our poisoning attack described in the paper “Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization.”
You can access the code in this link.
“Exploiting Correlations to Detect False Data Injections in Low-Density Wireless Sensor Networks” will be presented at 5th ACM Cyber-Physical Systems Security Workshop 2019.
Aaron will be presenting a paper based on his MSc thesis work “Exploiting Correlations to Detect False Data Injections in Low-Density Wireless Sensor Networks” at 5th ACM CPSS 2019, co-located workshop with ACM ASIACCS.