Search Results for author:
Found 14 papers, 7 papers with code
FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells
Moreover, a novel frequency domain denoising network, named FDNet, is proposed for astrocyte segmentation.
Large Language Models Based Fuzzing Techniques: A Survey
In the modern era where software plays a pivotal role, software security and vulnerability analysis have become essential for software development.
Benchmarking Transferable Adversarial Attacks
The robustness of deep learning models against adversarial attacks remains a pivotal concern.
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
With the functional and characteristic similarity analysis, we introduce a novel gradient editing (GE) mechanism and verify its feasibility in generating transferable samples on various models.
FairCompass: Operationalising Fairness in Machine Learning
As artificial intelligence (AI) increasingly becomes an integral part of our societal and individual activities, there is a growing imperative to develop responsible AI solutions.
MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks
To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome.
Code Ownership in Open-Source AI Software Security
As open-source AI software projects become an integral component in the AI software development, it is critical to develop a novel methods to ensure and measure the security of the open-source projects for developers.
DANAA: Towards transferable attacks with double adversarial neuron attribution
While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments.
Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
In particular, we experiment with twelve uncertainty estimation methods and four LLMs on four prominent natural language processing (NLP) tasks to investigate to what extent uncertainty estimation techniques could help characterize the prediction risks of LLMs.
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge.
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding.
A Survey on Data-driven Software Vulnerability Assessment and Prioritization
Software Vulnerabilities (SVs) are increasing in complexity and scale, posing great security risks to many software systems.
Saec: Similarity-Aware Embedding Compression in Recommendation Systems
Production recommendation systems rely on embedding methods to represent various features.
Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network
The proposed framework for this challenge has four steps: preprocessing, feature extraction, training and validation.
