1 code implementation • 5 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
no code implementations • 9 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.
no code implementations • 15 Dec 2023 • Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa, Mujie Liu, Vidya Saikrishna, Jiangang Ma, Feng Xia
Many multivariate time series anomaly detection frameworks have been proposed and widely applied.
no code implementations • 6 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).
no code implementations • 30 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.
no code implementations • 26 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.
no code implementations • 25 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.
no code implementations • 24 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.
no code implementations • 15 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.
no code implementations • 21 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).
no code implementations • 22 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.
no code implementations • 24 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.
no code implementations • 21 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.
no code implementations • 11 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.
no code implementations • 17 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.
no code implementations • 10 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.
no code implementations • 3 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.