Deep Fact Validation

Provides web credibility models (Likert scale) to assign a trustworthiness score to a given website.

In this paper, we introduce SAShA, a new attack strategy that leverages semantic features extracted from a knowledge graph in order to strengthen the efficacy of the attack to standard CF models. We performed an extensive experimental evaluation in order to investigate whether SAShA is more effective than baseline attacks against CF models by ta…

Trustworthiness Monitoring & Assessment Framework

a matrix to provide the clarified definition and relationship information of trustworthiness characteristics between in the AI/ML standards

Codes and Datasets for our WSDM 2022 Paper: "MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs"

In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks like PGD adversarial attack.

Independent continuation of a project from AstonHack 2017

Visualization and embedding of large datasets using various Dimensionality Reduction (DR) techniques such as t-SNE, UMAP, PaCMAP & IVHD. Implementation of custom metrics to assess DR quality with complete explaination and workflow.

Website for health data science at KDD 2021

Component M - Trustworthiness Monitoring & Assessment Framework

Component K - Trustworthiness Monitoring & Assessment Framework

Secure and trustworthy mobile AI.

An Assurance Process for Big Data Trustworthiness - Marco Anisetti, Claudio A. Ardagna, Filippo Berto

In this work, we provide 24 combinations of attack/defense strategies, and visual-based recommenders to 1) access performance alteration on recommendation and 2) empirically verify the effect on final users through offline visual metrics.

This repository is an implementation of the paper "Trustworthy Medical Image Segmentation with improved performance for in-distribution samples" published in Neural Networks.

Proposal of a novel adversarial attack approach, called Target Adversarial Attack against Multimedia Recommender Systems (TAaMR), to investigate the modification of MR behavior when the images of a category of low recommended products (e.g., socks) are perturbed to misclassify the deep neural classifier towards the class of more recommended prod…

Proof of Freshness: collate proof of an authorship date.