2 citations · 7 across the 12 of their papers we have counts for
10 papers · 1 filter
Mitigating Memorization in Text-to-Image Diffusion via Region-Aware Prompt Augmentation and Multimodal Copy Detection
Yunzhuo Chen, Jordan Vice, Naveed Akhtar +2
State-of-the-art text-to-image diffusion models can produce impressive visuals but may memorize and reproduce training images, creating copyright and privacy risks. Existing prompt…
CymbaDiff: Structured Spatial Diffusion for Sketch-based 3D Semantic Urban Scene Generation
Li Liang, Bo Miao, Xinyu Wang +3
Outdoor 3D semantic scene generation produces realistic and semantically rich environments for applications such as urban simulation and autonomous driving. However, advances in th…
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…
Skip Mamba Diffusion for Monocular 3D Semantic Scene Completion
Li Liang, Naveed Akhtar, Jordan Vice +2
3D semantic scene completion is critical for multiple downstream tasks in autonomous systems. It estimates missing geometric and semantic information in the acquired scene data. Du…
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…
BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models
Jordan Vice, Naveed Akhtar, Richard Hartley +1
The rise in popularity of text-to-image generative artificial intelligence (AI) has attracted widespread public interest. We demonstrate that this technology can be attacked to gen…