7 papers
Towards Verified and Targeted Explanations through Formal Methods
Hanchen David Wang, Diego Manzanas Lopez, Preston K. Robinette +3
As deep neural networks are deployed in safety-critical domains such as autonomous driving and medical diagnosis, stakeholders need explanations that are interpretable but also tru…
SUGAR: A Sweeter Spot for Generative Unlearning of Many Identities
Dung Thuy Nguyen, Quang Nguyen, Preston K. Robinette +3
Recent advances in 3D-aware generative models have enabled high-fidelity image synthesis of human identities. However, this progress raises urgent questions around user consent and…
Sanitizing Hidden Information with Diffusion Models
Preston K. Robinette, Daniel Moyer, Taylor T. Johnson
Information hiding is the process of embedding data within another form of data, often to conceal its existence or prevent unauthorized access. This process is commonly used in var…
Blind Visible Watermark Removal with Morphological Dilation
Preston K. Robinette, Taylor T. Johnson
Visible watermarks pose significant challenges for image restoration techniques, especially when the target background is unknown. Toward this end, we present MorphoMod, a novel me…
EditScout: Locating Forged Regions from Diffusion-based Edited Images with Multimodal LLM
Quang Nguyen, Truong Vu, Trong-Tung Nguyen +6
Image editing technologies are tools used to transform, adjust, remove, or otherwise alter images. Recent research has significantly improved the capabilities of image editing tool…
Formalizing Stateful Behavior Trees
Serena S. Serbinowska, Preston Robinette, Gabor Karsai +1
Behavior Trees (BTs) are high-level controllers that are useful in a variety of planning tasks and are gaining traction in robotic mission planning. As they gain popularity in safe…