1 citations · 1 across the 10 of their papers we have counts for
11 papers
UniGADD: Universal Generator-Agnostic Deepfake Detector
Bernard Tomczyk, Agil Aghasanli, Plamen Angelov
In this paper, we introduce Universal Generator-Agnostic Deepfake Detector (UniGADD), a novel method that employs supervised learning to achieve high accuracy on known deepfake gen…
ProtoMedX: Towards Explainable Multi-Modal Prototype Learning for Bone Health Classification
Alvaro Lopez Pellicer, Andre Mariucci, Plamen Angelov +2
Bone health studies are crucial in medical practice for the early detection and treatment of Osteopenia and Osteoporosis. Clinicians usually make a diagnosis based on densitometry…
Prototype-Based Continual Learning with Label-free Replay Buffer and Cluster Preservation Loss
Agil Aghasanli, Yi Li, Plamen Angelov
Continual learning techniques employ simple replay sample selection processes and use them during subsequent tasks. Typically, they rely on labeled data. In this paper, we depart f…
COMIX: Compositional Explanations using Prototypes
Sarath Sivaprasad, Dmitry Kangin, Plamen Angelov +1
Aligning machine representations with human understanding is key to improving interpretability of machine learning (ML) models. When classifying a new image, humans often explain t…
Complex-Cycle-Consistent Diffusion Model for Monaural Speech Enhancement
Yi Li, Yang Sun, Plamen Angelov
In this paper, we present a novel diffusion model-based monaural speech enhancement method. Our approach incorporates the separate estimation of speech spectra's magnitude and phas…
Self-Supervised Representation Learning for Adversarial Attack Detection
Yi Li, Plamen Angelov, Neeraj Suri
Supervised learning-based adversarial attack detection methods rely on a large number of labeled data and suffer significant performance degradation when applying the trained model…
PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection
Alvaro Lopez Pellcier, Yi Li, Plamen Angelov
Deepfake techniques generate highly realistic data, making it challenging for humans to discern between actual and artificially generated images. Recent advancements in deep learni…
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection
Yi Li, Plamen Angelov, Zhengxin Yu +2
As the development of deep learning techniques in autonomous landing systems continues to grow, one of the major challenges is trust and security in the face of possible adversaria…
IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data
Ziyang Zhang, Plamen Angelov, Dmitry Kangin +1
In this paper, we address two critical challenges in the domain of flood detection: the computational expense of large-scale time series change detection and the lack of interpreta…
Unsupervised Domain Adaptation within Deep Foundation Latent Spaces
Dmitry Kangin, Plamen Angelov
The vision transformer-based foundation models, such as ViT or Dino-V2, are aimed at solving problems with little or no finetuning of features. Using a setting of prototypical netw…
Marine Debris Detection in Satellite Surveillance using Attention Mechanisms
Ao Shen, Yijie Zhu, Richard Jiang
Marine debris is an important issue for environmental protection, but current methods for locating marine debris are yet limited. In order to achieve higher efficiency and wider ap…