2 citations · 3 across the 13 of their papers we have counts for
13 papers
ReACT-CLIP: Response-Aware Test-Time Defense for Vision--Language Models
Hashmat Shadab Malik, Toluwani Aremu, Samuele Poppi +2
Training-free test-time defenses offer a practical way to improve the adversarial robustness of CLIP-style vision--language models without modifying the pretrained model. However,…
CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs
Hashmat Shadab Malik, Anees Ur Rehman Hashmi, Numan Saeed +3
Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text judged only by its final answer…
Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models
Hashmat Shadab Malik, Muzammal Naseer, Salman Khan
Multimodal Large Language Models integrate visual perception into language reasoning, introducing a continuous attack surface susceptible to adversarial attacks. Prior work on MLLM…
Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models
Hashmat Shadab Malik, Muzammal Naseer, Salman Khan
Vision-language models (VLMs) such as CLIP show strong zero-shot generalization but remain highly vulnerable to adversarial attacks. Adversarial training improves robustness but is…
Investigating Adversarial Robustness of Multi-modal Large Language Models
Hashmat Shadab Malik, Muzammal Naseer, Salman Khan
Multi-modal Large Language Models (MLLMs) achieve strong performance on vision-language tasks, but incorporating visual inputs through a vision encoder (e.g., CLIP) substantially e…
How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark
Roba Al Majzoub, Hashmat Malik, Muzammal Naseer +4
Recently, histopathology vision-language foundation models (VLMs) have gained popularity due to their enhanced performance and generalizability across different downstream tasks. H…
Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathology
Hashmat Shadab Malik, Shahina Kunhimon, Muzammal Naseer +2
Adversarial attacks pose significant challenges for vision models in critical fields like healthcare, where reliability is essential. Although adversarial training has been well st…
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…
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…
On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models
Hashmat Shadab Malik, Numan Saeed, Asif Hanif +4
Volumetric medical segmentation models have achieved significant success on organ and tumor-based segmentation tasks in recent years. However, their vulnerability to adversarial at…
ObjectCompose: Evaluating Resilience of Vision-Based Models on Object-to-Background Compositional Changes
Hashmat Shadab Malik, Muhammad Huzaifa, Muzammal Naseer +2
Given the large-scale multi-modal training of recent vision-based models and their generalization capabilities, understanding the extent of their robustness is critical for their r…
Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations
Hashmat Shadab Malik, Shahina K Kunhimon, Muzammal Naseer +2
Transferable adversarial attacks optimize adversaries from a pretrained surrogate model and known label space to fool the unknown black-box models. Therefore, these attacks are res…
Object Detection in Aerial Images: What Improves the Accuracy?
Hashmat Shadab Malik, Ikboljon Sobirov, Abdelrahman Mohamed
Object detection is a challenging and popular computer vision problem. The problem is even more challenging in aerial images due to significant variation in scale and viewpoint in…