12 papers
The Density of Thought: Predicting and Steering Reasoning in Large Language Models
Tanush Kalhan, Anupam Chattopadhyay, Sriram Ranga +1
We investigate whether variation in information density across chain-of-thought (CoT) reasoning steps predicts answer correctness and can serve as a controllable inference-time tar…
A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization
Anadi Goyal, Nandish Chattopadhyay, Anupam Chattopadhyay +1
Vision Transformers (ViTs) increasingly rely on input-adaptive inference, such as token pruning and early halting, to meet energy and latency budgets. This survey examines a recent…
MOAT: Model-Agnostic Randomized Transformations for preventing Efficiency Degradation Attacks on ViTs
Anadi Goyal, Nandish Chattopadhyay, Chandan Karfa +2
To adopt the Vision Transformers (ViTs) in resource-constrained environment, token pruning is widely used to reduce computational cost without impacting accuracy. However, adversar…
On the Limits of Support-Preserving Alignment and Bounded Filtering
Aryan Dutt, Rui Mao, Anupam Chattopadhyay
We study whether alignment schemes that reshape a base model's output distribution, combined with bounded safety filters, can drive the probability of harmful behavior to zero in m…
Vaporizer: Breaking Watermarking Schemes for Large Language Model Outputs
Jonathan Hong Jin Ng, Anh Tu Ngo, Anupam Chattopadhyay
In this paper, we investigate the recent state-of-the-art schemes for watermarking large language models (LLMs) outputs. These techniques are claimed to be robust, scalable and pro…
NutVLM: A Self-Adaptive Defense Framework against Full-Dimension Attacks for Vision Language Models in Autonomous Driving
Xiaoxu Peng, Dong Zhou, Jianwen Zhang +3
Vision Language Models (VLMs) have advanced perception in autonomous driving (AD), but they remain vulnerable to adversarial threats. These risks range from localized physical patc…
STRAP-ViT: Segregated Tokens with Randomized -- Transformations for Defense against Adversarial Patches in ViTs
Nandish Chattopadhyay, Anadi Goyal, Chandan Karfa +1
Adversarial patches are physically realizable localized noise, which are able to hijack Vision Transformers (ViT) self-attention, pulling focus toward a small, high-contrast region…
Learning Nonlinearity of Boolean Functions: An Experimentation with Neural Networks
Sriram Ranga, Nandish Chattopadhyay, Anupam Chattopadhyay
This paper investigates the learnability of the nonlinearity property of Boolean functions using neural networks. We train encoder style deep neural networks to learn to predict th…
Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation
Anh Tu Ngo, Chuan Song Heng, Nandish Chattopadhyay +1
Deep Neural Networks (DNNs) have gained considerable traction in recent years due to the unparalleled results they gathered. However, the cost behind training such sophisticated mo…
BlockDoor: Blocking Backdoor Based Watermarks in Deep Neural Networks
Yi Hao Puah, Anh Tu Ngo, Nandish Chattopadhyay +1
Adoption of machine learning models across industries have turned Neural Networks (DNNs) into a prized Intellectual Property (IP), which needs to be protected from being stolen or…
Fast classical simulation of qubit-qudit hybrid systems
Haemanth Velmurugan, Arnav Das, Turbasu Chatterjee +3
Simulating quantum circuits is a computationally intensive task that relies heavily on tensor products and matrix multiplications, which can be inefficient. Recent advancements, el…
Adversarial Attacks and Dimensionality in Text Classifiers
Nandish Chattopadhyay, Atreya Goswami, Anupam Chattopadhyay
Adversarial attacks on machine learning algorithms have been a key deterrent to the adoption of AI in many real-world use cases. They significantly undermine the ability of high-pe…