Search Results for author: Ilia Shumailov

Found 17 papers, 2 papers with code

On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning

no code implementations22 Oct 2021 Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot

Machine unlearning, i. e. having a model forget about some of its training data, has become increasingly more important as privacy legislation promotes variants of the right-to-be-forgotten.

Rapid Model Architecture Adaption for Meta-Learning

no code implementations10 Sep 2021 Yiren Zhao, Xitong Gao, Ilia Shumailov, Nicolo Fusi, Robert Mullins

H-Meta-NAS shows a Pareto dominance compared to a variety of NAS and manual baselines in popular few-shot learning benchmarks with various hardware platforms and constraints.

Few-Shot Learning

Bad Characters: Imperceptible NLP Attacks

no code implementations18 Jun 2021 Nicholas Boucher, Ilia Shumailov, Ross Anderson, Nicolas Papernot

In this paper, we explore a large class of adversarial examples that can be used to attack text-based models in a black-box setting without making any human-perceptible visual modification to inputs.

Machine Translation

Markpainting: Adversarial Machine Learning meets Inpainting

1 code implementation1 Jun 2021 David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross Anderson

Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture editing and retouching.

Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems

no code implementations18 Apr 2021 Yue Gao, Ilia Shumailov, Kassem Fawaz

As real-world images come in varying sizes, the machine learning model is part of a larger system that includes an upstream image scaling algorithm.

Nudge Attacks on Point-Cloud DNNs

no code implementations22 Nov 2020 Yiren Zhao, Ilia Shumailov, Robert Mullins, Ross Anderson

The wide adaption of 3D point-cloud data in safety-critical applications such as autonomous driving makes adversarial samples a real threat.

Autonomous Driving

On Attribution of Deepfakes

no code implementations20 Aug 2020 Baiwu Zhang, Jin Peng Zhou, Ilia Shumailov, Nicolas Papernot

We discuss the ethical implications of our work, identify where our technique can be used, and highlight that a more meaningful legislative framework is required for a more transparent and ethical use of generative modeling.

DeepFake Detection Face Generation +2

Sponge Examples: Energy-Latency Attacks on Neural Networks

1 code implementation5 Jun 2020 Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, Ross Anderson

The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs.

Autonomous Vehicles

Towards Certifiable Adversarial Sample Detection

no code implementations20 Feb 2020 Ilia Shumailov, Yiren Zhao, Robert Mullins, Ross Anderson

Convolutional Neural Networks (CNNs) are deployed in more and more classification systems, but adversarial samples can be maliciously crafted to trick them, and are becoming a real threat.

Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information

no code implementations6 Sep 2019 Yiren Zhao, Ilia Shumailov, Han Cui, Xitong Gao, Robert Mullins, Ross Anderson

In this work, we show how such samples can be generalised from White-box and Grey-box attacks to a strong Black-box case, where the attacker has no knowledge of the agents, their training parameters and their training methods.

Time Series

Hearing your touch: A new acoustic side channel on smartphones

no code implementations26 Mar 2019 Ilia Shumailov, Laurent Simon, Jeff Yan, Ross Anderson

We found the device's microphone(s) can recover this wave and "hear" the finger's touch, and the wave's distortions are characteristic of the tap's location on the screen.

Sitatapatra: Blocking the Transfer of Adversarial Samples

no code implementations23 Jan 2019 Ilia Shumailov, Xitong Gao, Yiren Zhao, Robert Mullins, Ross Anderson, Cheng-Zhong Xu

Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision.

General Classification

Towards Automatic Discovery of Cybercrime Supply Chains

no code implementations2 Dec 2018 Rasika Bhalerao, Maxwell Aliapoulios, Ilia Shumailov, Sadia Afroz, Damon McCoy

Our analysis of the automatically generated supply chains demonstrates underlying connections between products and services within these forums.

The Taboo Trap: Behavioural Detection of Adversarial Samples

no code implementations18 Nov 2018 Ilia Shumailov, Yiren Zhao, Robert Mullins, Ross Anderson

Most existing detection mechanisms against adversarial attacksimpose significant costs, either by using additional classifiers to spot adversarial samples, or by requiring the DNN to be restructured.

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