18 citations · 25 across the 10 of their papers we have counts for
10 papers
Emergent Morphing Attack Detection in Open Multi-modal Large Language Models
Marija Ivanovska, Vitomir Štruc
Face morphing attacks threaten biometric verification, yet most morphing attack detection (MAD) systems require task-specific training and generalize poorly to unseen attack types.…
Second Competition on Presentation Attack Detection on ID Card
Juan E. Tapia, Mario Nieto, Juan M. Espin +30
This work summarises and reports the results of the second Presentation Attack Detection competition on ID cards. This new version includes new elements compared to the previous on…
SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning
Marija Ivanovska, Leon Todorov, Naser Damer +3
With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in…
MADation: Face Morphing Attack Detection with Foundation Models
Eduarda Caldeira, Guray Ozgur, Tahar Chettaoui +5
Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to cre…
TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models
Marija Ivanovska, Vitomir Struc, Janez Pers
Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact. Computer vision-based algorithms in partic…
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models
Marija Ivanovska, Vitomir Štruc
The detection of malicious deepfakes is a constantly evolving problem that requires continuous monitoring of detectors to ensure they can detect image manipulations generated by th…
Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models
Marija Ivanovska, Vitomir Štruc
Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's…
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
Marco Huber, Fadi Boutros, Anh Thi Luu +16
This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Con…
Face Morphing Attack Detection Using Privacy-Aware Training Data
Marija Ivanovska, Andrej Kronovšek, Peter Peer +2
Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single mor…
Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection
Marija Ivanovska, Vitomir Štruc
We propose a novel reconstruction-based model for anomaly detection, called Y-GAN. The model consists of a Y-shaped auto-encoder and represents images in two separate latent spaces…