324 research outputs found
Ethical Decision-making for Autonomous Driving based on LSTM Trajectory Prediction Network
The development of autonomous vehicles has brought a great impact and changes
to the transportation industry, offering numerous benefits in terms of safety
and efficiency. However, one of the key challenges that autonomous driving
faces is how to make ethical decisions in complex situations. To address this
issue, in this article, a novel trajectory prediction method is proposed to
achieve ethical decision-making for autonomous driving. Ethical considerations
are integrated into the decision-making process of autonomous vehicles by
quantifying the utility principle and incorporating them into mathematical
formulas. Furthermore, trajectory prediction is optimized using LSTM network
with an attention module, resulting in improved accuracy and reliability in
trajectory planning and selection. Through extensive simulation experiments, we
demonstrate the effectiveness of the proposed method in making ethical
decisions and selecting optimal trajectories.Comment: 7 pages, 4 figure
Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems
As a promising candidate to complement traditional biometric modalities,
brain biometrics using electroencephalography (EEG) data has received a
widespread attention in recent years. However, compared with existing
biometrics such as fingerprints and face recognition, research on EEG
biometrics is still in its infant stage. Most of the studies focus on either
designing signal elicitation protocols from the perspective of neuroscience or
developing feature extraction and classification algorithms from the viewpoint
of machine learning. These studies have laid the ground for the feasibility of
using EEG as a biometric authentication modality, but they have also raised
security and privacy concerns as EEG data contains sensitive information.
Existing research has used hash functions and cryptographic schemes to protect
EEG data, but they do not provide functions for revoking compromised templates
as in cancellable template design. This paper proposes the first cancellable
EEG template design for privacy-preserving EEG-based authentication systems,
which can protect raw EEG signals containing sensitive privacy information
(e.g., identity, health and cognitive status). A novel cancellable EEG template
is developed based on EEG graph features and a non-invertible transform. The
proposed transformation provides cancellable templates, while taking advantage
of EEG elicitation protocol fusion to enhance biometric performance. The
proposed authentication system offers equivalent authentication performance
(8.58\% EER on a public database) as in the non-transformed domain, while
protecting raw EEG data. Furthermore, we analyze the system's capacity for
resisting multiple attacks, and discuss some overlooked but critical issues and
possible pitfalls involving hill-climbing attacks, second attacks, and
classification-based authentication systems
PolyCosGraph:A Privacy-Preserving Cancelable EEG Biometric System
Recent findings confirm that biometric templates derived from electroencephalography (EEG) signals contain sensitive information about registered users, such as age, gender, cognitive ability, mental status and health information. Existing privacy-preserving methods such as hash function and fuzzy commitment are not cancelable, where raw biometric features are vulnerable to hill-climbing attacks. To address this issue, we propose the PolyCosGraph, a system based on Polynomial transformation embedding Cosine functions with Graph features of EEG signals, which is a privacy-preserving and cancelable template design that protects EEG features and system security against multiple attacks. In addition, a template corrupting process is designed to further enhance the security of the system, and a corresponding matching algorithm is developed. Even when the transformed template is compromised, attackers cannot retrieve raw EEG features and the compromised template can be revoked. The proposed system achieves the authentication performance of 1.49% EER with a resting state protocol, 0.68% EER with a motor imagery task, and 0.46% EER under a watching movie condition, which is equivalent to that in the non-encrypted domain. Security analysis demonstrates that our system is resistant to attacks via record multiplicity, preimage attacks, hill-climbing attacks, second attacks and brute force attacks.</p
Cancellable Deep Learning Framework for EEG Biometrics
EEG-based biometric systems verify the identity of a user by comparing the probe to a reference EEG template of the claimed user enrolled in the system, or by classifying the probe against a user verification model stored in the system. These approaches are often referred to as template-based and model-based methods, respectively. Compared with template-based methods, model-based methods, especially those based on deep learning models, tend to provide enhanced performance and more flexible applications. However, there is no public research report on the security and cancellability issue for model-based approaches. This becomes a critical issue considering the growing popularity of deep learning in EEG biometric applications. In this study, we investigate the security issue of deep learning model-based EEG biometric systems, and demonstrate that model inversion attacks post a threat for such model-based systems. That is to say, an adversary can produce synthetic data based on the output and parameters of the user verification model to gain unauthorized access by the system. We propose a cancellable deep learning framework to defend against such attacks and protect system security. The framework utilizes a generative adversarial network to approximate a non-invertible transformation whose parameters can be changed to produce different data distributions. A user verification model is then trained using output generated from the generator model, while information about the transformation is discarded. The proposed framework is able to revoke compromised models to defend against hill climbing attacks and model inversion attacks. Evaluation results show that the proposed method, while being cancellable, achieves better verification performance than the template-based methods and state-of-the-art non-cancellable deep learning methods
Graph Neural Network Based Method for Path Planning Problem
Sampling-based path planning is a widely used method in robotics,
particularly in high-dimensional state space. Among the whole process of the
path planning, collision detection is the most time-consuming operation. In
this paper, we propose a learning-based path planning method that aims to
reduce the number of collision detection. We develop an efficient neural
network model based on Graph Neural Networks (GNN) and use the environment map
as input. The model outputs weights for each neighbor based on the input and
current vertex information, which are used to guide the planner in avoiding
obstacles. We evaluate the proposed method's efficiency through simulated
random worlds and real-world experiments, respectively. The results demonstrate
that the proposed method significantly reduces the number of collision
detection and improves the path planning speed in high-dimensional
environments
Mani-GPT: A Generative Model for Interactive Robotic Manipulation
In real-world scenarios, human dialogues are multi-round and diverse.
Furthermore, human instructions can be unclear and human responses are
unrestricted. Interactive robots face difficulties in understanding human
intents and generating suitable strategies for assisting individuals through
manipulation. In this article, we propose Mani-GPT, a Generative Pre-trained
Transformer (GPT) for interactive robotic manipulation. The proposed model has
the ability to understand the environment through object information,
understand human intent through dialogues, generate natural language responses
to human input, and generate appropriate manipulation plans to assist the
human. This makes the human-robot interaction more natural and humanized. In
our experiment, Mani-GPT outperforms existing algorithms with an accuracy of
84.6% in intent recognition and decision-making for actions. Furthermore, it
demonstrates satisfying performance in real-world dialogue tests with users,
achieving an average response accuracy of 70%
MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement
Scoliosis diagnosis and assessment depend largely on the measurement of the
Cobb angle in spine X-ray images. With the emergence of deep learning
techniques that employ landmark detection, tilt prediction, and spine
segmentation, automated Cobb angle measurement has become increasingly popular.
However, these methods encounter difficulties such as high noise sensitivity,
intricate computational procedures, and exclusive reliance on a single type of
morphological information. In this paper, we introduce the Multiple
Morphology-Aware Network (MMA-Net), a novel framework that improves Cobb angle
measurement accuracy by integrating multiple spine morphology as attention
information. In the MMA-Net, we first feed spine X-ray images into the
segmentation network to produce multiple morphological information (spine
region, centerline, and boundary) and then concatenate the original X-ray image
with the resulting segmentation maps as input for the regression module to
perform precise Cobb angle measurement. Furthermore, we devise joint loss
functions for our segmentation and regression network training, respectively.
We evaluate our method on the AASCE challenge dataset and achieve superior
performance with the SMAPE of 7.28% and the MAE of 3.18{\deg}, indicating a
strong competitiveness compared to other outstanding methods. Consequently, we
can offer clinicians automated, efficient, and reliable Cobb angle measurement
Biometrics based privacy-preserving authentication and mobile template protection
Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template
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