Search Results for author:
Found 13 papers, 5 papers with code
Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task
Intention-based Human-Robot Interaction (HRI) systems allow robots to perceive and interpret user actions to proactively interact with humans and adapt to their behavior.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications.
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
Semantic understanding of roadways is a key enabling factor for safe autonomous driving.
Membership Inference Attacks on DNNs using Adversarial Perturbations
Secondly, we utilize the framework to propose two novel attacks: (1) Adversarial Membership Inference Attack (AMIA) efficiently utilizes the membership and the non-membership information of the subjects while adversarially minimizing a novel loss function, achieving 6% TPR on both Fashion-MNIST and MNIST datasets; and (2) Enhanced AMIA (E-AMIA) combines EMIA and AMIA to achieve 8% and 4% TPRs on Fashion-MNIST and MNIST datasets respectively, at 1% FPR.
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
In this paper, we propose a systematic methodology for developing robust models for surgical tool detection using noisy data.
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models
Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep crowd-flow prediction models in particular have remained largely unexplored.
Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses
Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s).
Visual Heart Rate Estimation from RGB Facial Video using Spectral Reflectance
Estimation of the Heart rate from the facial video has a number of applications in the medical and fitness industries.
Snapture -- A Novel Neural Architecture for Combined Static and Dynamic Hand Gesture Recognition
Our architecture enables learning both static and dynamic gestures: by capturing a so-called "snapshot" of the gesture performance at its peak, we integrate the hand pose along with the dynamic movement.
HaS-Nets: A Heal and Select Mechanism to Defend DNNs Against Backdoor Attacks for Data Collection Scenarios
We have witnessed the continuing arms race between backdoor attacks and the corresponding defense strategies on Deep Neural Networks (DNNs).
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
To address this limitation, decision-based attacks have been proposed which can estimate the model but they require several thousand queries to generate a single untargeted attack image.
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
In this paper, we introduce a novel technique based on the Secure Selective Convolutional (SSC) techniques in the training loop that increases the robustness of a given DNN by allowing it to learn the data distribution based on the important edges in the input image.
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs).
