7 papers · 1 filter
Quantum-Enhanced Adversarial Robustness in Artificial Intelligence
Jaydip Sen
Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses significant challenges to reliab…
Adversarial Text Generation with Dynamic Contextual Perturbation
Hetvi Waghela, Jaydip Sen, Sneha Rakshit +1
Adversarial attacks on Natural Language Processing (NLP) models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while m…
Adversarial Robustness through Dynamic Ensemble Learning
Hetvi Waghela, Jaydip Sen, Sneha Rakshit
Adversarial attacks pose a significant threat to the reliability of pre-trained language models (PLMs) such as GPT, BERT, RoBERTa, and T5. This paper presents Adversarial Robustnes…
Robust Image Classification: Defensive Strategies against FGSM and PGD Adversarial Attacks
Hetvi Waghela, Jaydip Sen, Sneha Rakshit
Adversarial attacks, particularly the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) pose significant threats to the robustness of deep learning models in im…
Privacy in Federated Learning
Jaydip Sen, Hetvi Waghela, Sneha Rakshit
Federated Learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw dat…
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation
Hetvi Waghela, Jaydip Sen, Sneha Rakshit
In this paper, we introduce an enhanced textual adversarial attack method, known as Saliency Attention and Semantic Similarity driven adversarial Perturbation (SASSP). The proposed…