7 papers
GDB-Reward: From Evaluation Metrics to Training Rewards for Graphic Design
Adrienne Deganutti, Purvanshi Mehta, Simon Hadfield +1
Text-to-image models excel at natural image synthesis but struggle with graphic design, where success depends on satisfying precise constraints on typography, layout, color, and vi…
SpooFL: Spoofing Federated Learning
Isaac Baglin, Xiatian Zhu, Simon Hadfield
Traditional defenses against Deep Leakage (DL) attacks in Federated Learning (FL) primarily focus on obfuscation, introducing noise, transformations or encryption to degrade an att…
Deep Leakage with Generative Flow Matching Denoiser
Isaac Baglin, Xiatian Zhu, Simon Hadfield
Federated Learning (FL) has emerged as a powerful paradigm for decentralized model training, yet it remains vulnerable to deep leakage (DL) attacks that reconstruct private client…
EvtSlowTV -- A Large and Diverse Dataset for Event-Based Depth Estimation
Sadiq Layi Macaulay, Nimet Kaygusuz, Simon Hadfield
Event cameras, with their high dynamic range (HDR) and low latency, offer a promising alternative for robust depth estimation in challenging environments. However, many event-based…
DANTE-AD: Dual-Vision Attention Network for Long-Term Audio Description
Adrienne Deganutti, Simon Hadfield, Andrew Gilbert
Audio Description is a narrated commentary designed to aid vision-impaired audiences in perceiving key visual elements in a video. While short-form video understanding has advanced…
Differential Adjusted Parity for Learning Fair Representations
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
The development of fair and unbiased machine learning models remains an ongoing objective for researchers in the field of artificial intelligence. We introduce the Differential Adj…
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses
Isaac Baglin, Xiatian Zhu, Simon Hadfield
Federated Learning is a privacy preserving decentralized machine learning paradigm designed to collaboratively train models across multiple clients by exchanging gradients to the s…