158 citations · 271 across the 18 of their papers we have counts for
22 papers
Human-Producible Adversarial Examples
David Khachaturov, Yue Gao, Ilia Shumailov +3
Visual adversarial examples have so far been restricted to pixel-level image manipulations in the digital world, or have required sophisticated equipment such as 2D or 3D printers…
Machine Learning needs Better Randomness Standards: Randomised Smoothing and PRNG-based attacks
Pranav Dahiya, Ilia Shumailov, Ross Anderson
Randomness supports many critical functions in the field of machine learning (ML) including optimisation, data selection, privacy, and security. ML systems outsource the task of ge…
When Vision Fails: Text Attacks Against ViT and OCR
Nicholas Boucher, Jenny Blessing, Ilia Shumailov +2
Text-based machine learning models are vulnerable to an emerging class of Unicode-based adversarial examples capable of tricking a model into misreading text with potentially disas…
The Curse of Recursion: Training on Generated Data Makes Models Forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao +3
Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT in…
Boosting Big Brother: Attacking Search Engines with Encodings
Nicholas Boucher, Luca Pajola, Ilia Shumailov +2
Search engines are vulnerable to attacks against indexing and searching via text encoding manipulation. By imperceptibly perturbing text using uncommon encoded representations, adv…
ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks
Eleanor Clifford, Ilia Shumailov, Yiren Zhao +2
Early backdoor attacks against machine learning set off an arms race in attack and defence development. Defences have since appeared demonstrating some ability to detect backdoors…
ExtremeBB: A Database for Large-Scale Research into Online Hate, Harassment, the Manosphere and Extremism
Anh V. Vu, Lydia Wilson, Yi Ting Chua +2
We introduce ExtremeBB, a textual database of over 53.5M posts made by 38.5k users on 12 extremist bulletin board forums promoting online hate, harassment, the manosphere and other…
Bad Characters: Imperceptible NLP Attacks
Nicholas Boucher, Ilia Shumailov, Ross Anderson +1
Several years of research have shown that machine-learning systems are vulnerable to adversarial examples, both in theory and in practice. Until now, such attacks have primarily ta…
Markpainting: Adversarial Machine Learning meets Inpainting
David Khachaturov, Ilia Shumailov, Yiren Zhao +2
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…
Manipulating SGD with Data Ordering Attacks
Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan +4
Machine learning is vulnerable to a wide variety of attacks. It is now well understood that by changing the underlying data distribution, an adversary can poison the model trained…
Hey Alexa what did I just type? Decoding smartphone sounds with a voice assistant
Almos Zarandy, Ilia Shumailov, Ross Anderson
Voice assistants are now ubiquitous and listen in on our everyday lives. Ever since they became commercially available, privacy advocates worried that the data they collect can be…
Nudge Attacks on Point-Cloud DNNs
Yiren Zhao, Ilia Shumailov, Robert Mullins +1
The wide adaption of 3D point-cloud data in safety-critical applications such as autonomous driving makes adversarial samples a real threat. Existing adversarial attacks on point c…
BatNet: Data transmission between smartphones over ultrasound
Almos Zarandy, Ilia Shumailov, Ross Anderson
In this paper, we present BatNet, a data transmission mechanism using ultrasound signals over the built-in speakers and microphones of smartphones. Using phase shift keying with an…
Snitches Get Stitches: On The Difficulty of Whistleblowing
Mansoor Ahmed-Rengers, Ross Anderson, Darija Halatova +1
One of the most critical security protocol problems for humans is when you are betraying a trust, perhaps for some higher purpose, and the world can turn against you if you're caug…
Sponge Examples: Energy-Latency Attacks on Neural Networks
Ilia Shumailov, Yiren Zhao, Daniel Bates +3
The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While this enabled us to train large-scale neural netw…
Towards Certifiable Adversarial Sample Detection
Ilia Shumailov, Yiren Zhao, Robert Mullins +1
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…
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information
Yiren Zhao, Ilia Shumailov, Han Cui +3
Recent research on reinforcement learning (RL) has suggested that trained agents are vulnerable to maliciously crafted adversarial samples. In this work, we show how such samples c…
Hearing your touch: A new acoustic side channel on smartphones
Ilia Shumailov, Laurent Simon, Jeff Yan +1
We present the first acoustic side-channel attack that recovers what users type on the virtual keyboard of their touch-screen smartphone or tablet. When a user taps the screen with…
Sitatapatra: Blocking the Transfer of Adversarial Samples
Ilia Shumailov, Xitong Gao, Yiren Zhao +3
Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision. However, they can be tricked into misclassifying specially crafted `adversari…
Tendrils of Crime: Visualizing the Diffusion of Stolen Bitcoins
Mansoor Ahmed-Rengers, Ilia Shumailov, Ross Anderson
The first six months of 2018 saw cryptocurrency thefts of $761 million, and the technology is also the latest and greatest tool for money laundering. This increase in crime has cau…
The Taboo Trap: Behavioural Detection of Adversarial Samples
Ilia Shumailov, Yiren Zhao, Robert Mullins +1
Deep Neural Networks (DNNs) have become a powerful toolfor a wide range of problems. Yet recent work has found an increasing variety of adversarial samplesthat can fool them. Most…
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression
Yiren Zhao, Ilia Shumailov, Robert Mullins +1
As deep neural networks (DNNs) become widely used, pruned and quantised models are becoming ubiquitous on edge devices; such compressed DNNs are popular for lowering computational…