1 citations · 1 across the 18 of their papers we have counts for
19 papers
Anti-Persona: Disrupting Unauthorized Identity Binding and Recognition in Personalized Vision--Language Models
Abhishek Basu, Fahad Shamshad, Karthik Nandakumar
Few-shot personalization enables large vision--language models (LVLMs) to learn user-specific visual concepts for applications such as personalized retrieval and subject-aware quer…
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…
Class-wise Contribution Estimation via Logit Maximization for Federated Learning
Asim Ukaye, Nurbek Tastan, Mubarak Abdu-Aguye +1
Federated learning (FL) enables collaborative learning of computer vision models, where privacy and regulatory constraints prevent centralizing data across devices or organizations…
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…
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…
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…
FaceAnonyMixer: Cancelable Faces via Identity Consistent Latent Space Mixing
Mohammed Talha Alam, Fahad Shamshad, Fakhri Karray +1
Advancements in face recognition (FR) technologies have amplified privacy concerns, necessitating methods that protect identity while maintaining recognition utility. Existing face…
Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models
Hashmat Shadab Malik, Fahad Shamshad, Muzammal Naseer +3
Multi-modal Large Language Models (MLLMs) excel in vision-language tasks but remain vulnerable to visual adversarial perturbations that can induce hallucinations, manipulate respon…
Introducing SDICE: An Index for Assessing Diversity of Synthetic Medical Datasets
Mohammed Talha Alam, Raza Imam, Mohammad Areeb Qazi +2
Advancements in generative modeling are pushing the state-of-the-art in synthetic medical image generation. These synthetic images can serve as an effective data augmentation metho…
PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning
Noor Hussein, Fahad Shamshad, Muzammal Naseer +1
Medical vision-language models (Med-VLMs) trained on large datasets of medical image-text pairs and later fine-tuned for specific tasks have emerged as a mainstream paradigm in med…
Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors
Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar
Deep learning-based face recognition (FR) systems pose significant privacy risks by tracking users without their consent. While adversarial attacks can protect privacy, they often…
BAPLe: Backdoor Attacks on Medical Foundational Models using Prompt Learning
Asif Hanif, Fahad Shamshad, Muhammad Awais +5
Medical foundation models are gaining prominence in the medical community for their ability to derive general representations from extensive collections of medical image-text pairs…
STEREO: A Two-Stage Framework for Adversarially Robust Concept Erasing from Text-to-Image Diffusion Models
Koushik Srivatsan, Fahad Shamshad, Muzammal Naseer +2
The rapid proliferation of large-scale text-to-image diffusion (T2ID) models has raised serious concerns about their potential misuse in generating harmful content. Although numero…
A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation
Mothilal Asokan, Joseph Geo Benjamin, Mohammad Yaqub +1
Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data…
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification
Naif Alkhunaizi, Faris Almalik, Rouqaiah Al-Refai +2
With the advent of large pre-trained transformer models, fine-tuning these models for various downstream tasks is a critical problem. Paucity of training data, the existence of dat…
Towards Evaluating the Robustness of Visual State Space Models
Hashmat Shadab Malik, Fahad Shamshad, Muzammal Naseer +3
Vision State Space Models (VSSMs), a novel architecture that combines the strengths of recurrent neural networks and latent variable models, have demonstrated remarkable performanc…