4 papers · 1 filter
Detection of Adversarial Attacks on Super-Resolvers Using Spectral Features
Emma J. Reid, Haley Duba-Sullivan, Tony G. Allen
The integration of deep learning models into image preprocessing pipelines such as super-resolution introduces a largely unexplored attack vector for adversaries targeting downstre…
Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
Haley Duba-Sullivan, Patxi Fernandez-Zelaia, Obaidullah Rahman +1
Computed tomography (CT) throughput is limited by scan time, which grows with both the number of projections acquired and the detector integration time for each projection. Reconst…
Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography
Timofey Efimov, Singanallur Venkatakrishnan, Maliha Hossain +2
Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to colle…
The Double-Edged Sword of Data-Driven Super-Resolution: Adversarial Super-Resolution Models
Haley Duba-Sullivan, Steven R. Young, Emma J. Reid
Data-driven super-resolution (SR) methods are often integrated into imaging pipelines as preprocessing steps to improve downstream tasks such as classification and detection. Howev…