2 citations · 5 across the 6 of their papers we have counts for
6 papers
On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations
Jordan Vice, Naveed Akhtar, Yansong Gao +2
Vision-Language Models (VLMs) are increasingly used as perceptual modules for visual content reasoning, including through captioning and DeepFake detection. In this work, we expose…
Exploring Bias in over 100 Text-to-Image Generative Models
Jordan Vice, Naveed Akhtar, Richard Hartley +1
We investigate bias trends in text-to-image generative models over time, focusing on the increasing availability of models through open platforms like Hugging Face. While these pla…
Safety Without Semantic Disruptions: Editing-free Safe Image Generation via Context-preserving Dual Latent Reconstruction
Jordan Vice, Naveed Akhtar, Mubarak Shah +2
Training multimodal generative models on large, uncurated datasets can result in users being exposed to harmful, unsafe and controversial or culturally-inappropriate outputs. While…
On the Fairness, Diversity and Reliability of Text-to-Image Generative Models
Jordan Vice, Naveed Akhtar, Leonid Sigal +2
The rapid proliferation of multimodal generative models has sparked critical discussions on their reliability, fairness and potential for misuse. While text-to-image models excel a…
Manipulating and Mitigating Generative Model Biases without Retraining
Jordan Vice, Naveed Akhtar, Richard Hartley +1
Text-to-image (T2I) generative models have gained increased popularity in the public domain. While boasting impressive user-guided generative abilities, their black-box nature expo…
Quantifying Bias in Text-to-Image Generative Models
Jordan Vice, Naveed Akhtar, Richard Hartley +1
Bias in text-to-image (T2I) models can propagate unfair social representations and may be used to aggressively market ideas or push controversial agendas. Existing T2I model bias e…