5 papers
Backdoor Attacks on Open Vocabulary Object Detectors via Multi-Modal Prompt Tuning
Ankita Raj, Chetan Arora
Open-vocabulary object detectors (OVODs) unify vision and language to detect arbitrary object categories based on text prompts, enabling strong zero-shot generalization to novel co…
Mimicking Human Visual Development for Learning Robust Image Representations
Ankita Raj, Kaashika Prajaapat, Tapan Kumar Gandhi +1
The human visual system is remarkably adept at adapting to changes in the input distribution; a capability modern convolutional neural networks (CNNs) still struggle to match. Draw…
Identifying Physically Realizable Triggers for Backdoored Face Recognition Networks
Ankita Raj, Ambar Pal, Chetan Arora
Backdoor attacks embed a hidden functionality into deep neural networks, causing the network to display anomalous behavior when activated by a predetermined pattern in the input Tr…
Assessing Risk of Stealing Proprietary Models for Medical Imaging Tasks
Ankita Raj, Harsh Swaika, Deepankar Varma +1
The success of deep learning in medical imaging applications has led several companies to deploy proprietary models in diagnostic workflows, offering monetized services. Even thoug…
Examining the Threat Landscape: Foundation Models and Model Stealing
Ankita Raj, Deepankar Varma, Chetan Arora
Foundation models (FMs) for computer vision learn rich and robust representations, enabling their adaptation to task/domain-specific deployments with little to no fine-tuning. Howe…