6 papers
MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense
Abhishek Bhardwaj, Arnav Doshi, Anusri Nagarajan +5
Time-series forecasting models remain vulnerable to gradient-based adversarial attacks while existing defense mechanisms typically incur a trade-off in robustness for bounded respo…
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…
Multi-Amateur Contrastive Decoding for Text Generation
Jaydip Sen, Subhasis Dasgupta, Hetvi Waghela
Contrastive Decoding (CD) has emerged as an effective inference-time strategy for enhancing open-ended text generation by exploiting the divergence in output probabilities between…
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…
Context-Enhanced Contrastive Search for Improved LLM Text Generation
Jaydip Sen, Rohit Pandey, Hetvi Waghela
Recently, Large Language Models (LLMs) have demonstrated remarkable advancements in Natural Language Processing (NLP). However, generating high-quality text that balances coherence…
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…