Search Results for author: Kristen Moore

Found 18 papers, 1 papers with code

An Investigation into Misuse of Java Security APIs by Large Language Models

no code implementations4 Apr 2024 Zahra Mousavi, Chadni Islam, Kristen Moore, Alsharif Abuadbba, Muhammad Ali Babar

We employ both automated and manual approaches to effectively detect security API misuse in the code generated by ChatGPT for these tasks.

Code Generation

Learning with Mixture of Prototypes for Out-of-Distribution Detection

1 code implementation5 Feb 2024 Haodong Lu, Dong Gong, Shuo Wang, Jason Xue, Lina Yao, Kristen Moore

To tackle these issues, we propose PrototypicAl Learning with a Mixture of prototypes (PALM) which models each class with multiple prototypes to capture the sample diversities, and learns more faithful and compact samples embeddings to enhance OOD detection.

Out-of-Distribution Detection Out of Distribution (OOD) Detection +1

SoK: Facial Deepfake Detectors

no code implementations9 Jan 2024 Binh M. Le, Jiwon Kim, Shahroz Tariq, Kristen Moore, Alsharif Abuadbba, Simon S. Woo

Our systematized analysis and experimentation lay the groundwork for a deeper understanding of deepfake detectors and their generalizability, paving the way for future research focused on creating detectors adept at countering various attack scenarios.

DeepFake Detection Face Swapping

DEFT: A new distance-based feature set for keystroke dynamics

no code implementations6 Oct 2023 Nuwan Kaluarachchi, Sevvandi Kandanaarachchi, Kristen Moore, Arathi Arakala

We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT).

DualVAE: Controlling Colours of Generated and Real Images

no code implementations30 May 2023 Keerth Rathakumar, David Liebowitz, Christian Walder, Kristen Moore, Salil S. Kanhere

The disentangled representation is obtained by two novel mechanisms: (i) a dual branch architecture that separates image colour attributes from geometric attributes, and (ii) a new ELBO that trains the combined colour and geometry representations.

Image Generation

Deepfake in the Metaverse: Security Implications for Virtual Gaming, Meetings, and Offices

no code implementations26 Mar 2023 Shahroz Tariq, Alsharif Abuadbba, Kristen Moore

This paper examines the security implications of deepfakes in the metaverse, specifically in the context of gaming, online meetings, and virtual offices.

Face Swapping

Why Do Facial Deepfake Detectors Fail?

no code implementations25 Feb 2023 Binh Le, Shahroz Tariq, Alsharif Abuadbba, Kristen Moore, Simon Woo

Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio.

DeepFake Detection Face Swapping +1

DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks

no code implementations24 Nov 2022 Seonhye Park, Alsharif Abuadbba, Shuo Wang, Kristen Moore, Yansong Gao, Hyoungshick Kim, Surya Nepal

In this study, we introduce DeepTaster, a novel DNN fingerprinting technique, to address scenarios where a victim's data is unlawfully used to build a suspect model.

Data Augmentation Transfer Learning

Deception for Cyber Defence: Challenges and Opportunities

no code implementations15 Aug 2022 David Liebowitz, Surya Nepal, Kristen Moore, Cody J. Christopher, Salil S. Kanhere, David Nguyen, Roelien C. Timmer, Michael Longland, Keerth Rathakumar

Deception is rapidly growing as an important tool for cyber defence, complementing existing perimeter security measures to rapidly detect breaches and data theft.

PublicCheck: Public Integrity Verification for Services of Run-time Deep Models

no code implementations21 Mar 2022 Shuo Wang, Sharif Abuadbba, Sidharth Agarwal, Kristen Moore, Ruoxi Sun, Minhui Xue, Surya Nepal, Seyit Camtepe, Salil Kanhere

Existing integrity verification approaches for deep models are designed for private verification (i. e., assuming the service provider is honest, with white-box access to model parameters).

Model Compression

Graph Lifelong Learning: A Survey

no code implementations22 Feb 2022 Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, Charu Aggarwal

Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure.

Graph Learning Recommendation Systems

SchemaDB: Structures in Relational Datasets

no code implementations24 Nov 2021 Cody James Christopher, Kristen Moore, David Liebowitz

In this paper we introduce the SchemaDB data-set; a collection of relational database schemata in both sql and graph formats.

Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception

no code implementations21 Nov 2021 Kristen Moore, Cody J. Christopher, David Liebowitz, Surya Nepal, Renee Selvey

Cyber deception is emerging as a promising approach to defending networks and systems against attackers and data thieves.

Language Modelling

A comprehensive solution to retrieval-based chatbot construction

no code implementations11 Jun 2021 Kristen Moore, Shenjun Zhong, Zhen He, Torsten Rudolf, Nils Fisher, Brandon Victor, Neha Jindal

In this paper we present the results of our experiments in training and deploying a self-supervised retrieval-based chatbot trained with contrastive learning for assisting customer support agents.

Binary Classification Chatbot +3

RAIDER: Reinforcement-aided Spear Phishing Detector

no code implementations17 May 2021 Keelan Evans, Alsharif Abuadbba, Tingmin Wu, Kristen Moore, Mohiuddin Ahmed, Ganna Pogrebna, Surya Nepal, Mike Johnstone

RAIDER also keeps the number of features to a minimum by selecting only the significant features to represent phishing emails and detect spear-phishing attacks.

Binary Classification reinforcement-learning +1

Robust Training Using Natural Transformation

no code implementations10 May 2021 Shuo Wang, Lingjuan Lyu, Surya Nepal, Carsten Rudolph, Marthie Grobler, Kristen Moore

We target attributes of the input images that are independent of the class identification, and manipulate those attributes to mimic real-world natural transformations (NaTra) of the inputs, which are then used to augment the training dataset of the image classifier.

Attribute Data Augmentation +2

OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning

no code implementations3 May 2021 Shuo Wang, Surya Nepal, Kristen Moore, Marthie Grobler, Carsten Rudolph, Alsharif Abuadbba

We introduce a new distributed/collaborative learning scheme to address communication overhead via latent compression, leveraging global data while providing privatization of local data without additional cost due to encryption or perturbation.

Disentanglement Federated Learning

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