Publications (17)
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…
Resolving Semantic Confusions for Improved Zero-Shot Detection
Sandipan Sarma, Sushil Kumar, Arijit Sur
Zero-shot detection (ZSD) is a challenging task where we aim to recognize and localize objects simultaneously, even when our model has not been trained with visual samples of a few…
Harnessing Multi-resolution and Multi-scale Attention for Underwater Image Restoration
Alik Pramanick, Arijit Sur, V. Vijaya Saradhi
Underwater imagery is often compromised by factors such as color distortion and low contrast, posing challenges for high-level vision tasks. Recent underwater image restoration (UI…
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…
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
Alik Pramanick, Utsav Bheda, Arijit Sur
Recently, transformers have captured significant interest in the area of single-image super-resolution tasks, demonstrating substantial gains in performance. Current models heavily…
Multi-Contextual Design of Convolutional Neural Network for Steganalysis
Brijesh Singh, Arijit Sur, Pinaki Mitra
In recent times, deep learning-based steganalysis classifiers became popular due to their state-of-the-art performance. Most deep steganalysis classifiers usually extract noise res…