5 papers
C-LEAD: Contrastive Learning for Enhanced Adversarial Defense
Suklav Ghosh, Sonal Kumar, Arijit Sur
Deep neural networks (DNNs) have achieved remarkable success in computer vision tasks such as image classification, segmentation, and object detection. However, they are vulnerable…
DGSSM: Diffusion guided state-space models for multimodal salient object detection
Suklav Ghosh, Arijit Sur, Pinaki Mitra
Salient object detection (SOD) requires modeling both long-range contextual dependencies and fine-grained structural details, which remains challenging for convolutional, transform…
Universal Adversarial Suffixes for Language Models Using Reinforcement Learning with Calibrated Reward
Sampriti Soor, Suklav Ghosh, Arijit Sur
Language models are vulnerable to short adversarial suffixes that can reliably alter predictions. Previous works usually find such suffixes with gradient search or rule-based metho…
Universal Adversarial Suffixes Using Calibrated Gumbel-Softmax Relaxation
Sampriti Soor, Suklav Ghosh, Arijit Sur
Language models (LMs) are often used as zero-shot or few-shot classifiers by scoring label words, but they remain fragile to adversarial prompts. Prior work typically optimizes tas…
Trans-defense: Transformer-based Denoiser for Adversarial Defense with Spatial-Frequency Domain Representation
Alik Pramanick, Mayank Bansal, Utkarsh Srivastava +2
In recent times, deep neural networks (DNNs) have been successfully adopted for various applications. Despite their notable achievements, it has become evident that DNNs are vulner…