1 citations · 1 across the 11 of their papers we have counts for
13 papers
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…
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…
PatchBlock: A Lightweight Defense Against Adversarial Patches for Embedded EdgeAI Devices
Nandish Chattopadhyay, Abdul Basit, Amira Guesmi +3
Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which re…
A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges
Nandish Chattopadhyay, Abdul Basit, Bassem Ouni +1
Adversarial attacks have emerged as a major challenge to the trustworthy deployment of machine learning models, particularly in computer vision applications. These attacks have a v…
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…
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…
Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks
Nandish Chattopadhyay, Amira Guesmi, Muhammad Shafique
Adversarial patch attacks pose a significant threat to the practical deployment of deep learning systems. However, existing research primarily focuses on image pre-processing defen…
DefensiveDR: Defending against Adversarial Patches using Dimensionality Reduction
Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif +2
Adversarial patch-based attacks have shown to be a major deterrent towards the reliable use of machine learning models. These attacks involve the strategic modification of localize…
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches
Nandish Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif +2
Adversarial attacks present a significant challenge to the dependable deployment of machine learning models, with patch-based attacks being particularly potent. These attacks intro…
Spatially Correlated Patterns in Adversarial Images
Nandish Chattopadhyay, Lionell Yip En Zhi, Bryan Tan Bing Xing +1
Adversarial attacks have proved to be the major impediment in the progress on research towards reliable machine learning solutions. Carefully crafted perturbations, imperceptible t…