3 papers
cs.LG2025
Embedding Hidden Adversarial Capabilities in Pre-Trained Diffusion Models
Lucas Beerens, Desmond J. Higham
We introduce a new attack paradigm that embeds hidden adversarial capabilities directly into diffusion models via fine-tuning, without altering their observable behavior or requiri…
cs.LG2024
Deceptive Diffusion: Generating Synthetic Adversarial Examples
Lucas Beerens, Catherine F. Higham, Desmond J. Higham
We introduce the concept of deceptive diffusion -- training a generative AI model to produce adversarial images. Whereas a traditional adversarial attack algorithm aims to perturb…
cs.AI2023
Adversarial Ink: Componentwise Backward Error Attacks on Deep Learning
Lucas Beerens, Desmond J. Higham
Deep neural networks are capable of state-of-the-art performance in many classification tasks. However, they are known to be vulnerable to adversarial attacks -- small perturbation…