18 citations · 68 across the 22 of their papers we have counts for
28 papers
SafeDiffusion-R1: Online Reward Steering for Safe Diffusion Post-Training
Komal Kumar, Ankan Deria, Abhishek Basu +3
Diffusion models have been widely studied for removing unsafe content learned during pre-training. Existing methods require expensive supervised data, either unsafe-text paired wit…
Towards Calibrating Prompt Tuning of Vision-Language Models
Ashshak Sharifdeen, Fahad Shamshad, Muhammad Akhtar Munir +6
Prompt tuning of large-scale vision-language models such as CLIP enables efficient task adaptation without updating model weights. However, it often leads to poor confidence calibr…
RAVEN: Erasing Invisible Watermarks via Novel View Synthesis
Fahad Shamshad, Nils Lukas, Karthik Nandakumar
Invisible watermarking has become a critical mechanism for authenticating AI-generated image content, with major platforms deploying watermarking schemes at scale. However, evaluat…
SPQR: A Multi-Dimensional Benchmark for Safety Alignment under Benign Model Adaptation
Mohammed Talha Alam, Nada Saadi, Fahad Shamshad +4
Text-to-image diffusion models can emit copyrighted, unsafe, or private content. Safety alignment aims to suppress specific concepts, yet evaluations seldom test whether safety per…
Calibration-Aware Prompt Learning for Medical Vision-Language Models
Abhishek Basu, Fahad Shamshad, Ashshak Sharifdeen +2
Medical Vision-Language Models (Med-VLMs) have demonstrated remarkable performance across diverse medical imaging tasks by leveraging large-scale image-text pretraining. However, t…
First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge
Fahad Shamshad, Tameem Bakr, Yahia Shaaban +3
Content watermarking is an important tool for the authentication and copyright protection of digital media. However, it is unclear whether existing watermarks are robust against ad…