209 citations · 216 across the 28 of their papers we have counts for
4 papers · 1 filter
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…
On quantifying and improving realism of images generated with diffusion
Yunzhuo Chen, Naveed Akhtar, Nur Al Hasan Haldar +1
Recent advances in diffusion models have led to a quantum leap in the quality of generative visual content. However, quantification of realism of the content is still challenging.…
Text-image guided Diffusion Model for generating Deepfake celebrity interactions
Yunzhuo Chen, Nur Al Hasan Haldar, Naveed Akhtar +1
Deepfake images are fast becoming a serious concern due to their realism. Diffusion models have recently demonstrated highly realistic visual content generation, which makes them a…
PRAT: PRofiling Adversarial aTtacks
Rahul Ambati, Naveed Akhtar, Ajmal Mian +1
Intrinsic susceptibility of deep learning to adversarial examples has led to a plethora of attack techniques with a broad common objective of fooling deep models. However, we find…