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cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping

Sanoojan Baliah, Yohan Abeysinghe, Rusiru Thushara +4

We present a training-free, plug-and-play method, namely VFace, for high-quality face swapping in videos. It can be seamlessly integrated with image-based face swapping approaches…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…