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arXiv 2023年

作者： Damer, Naser Fang, Meiling Siebke, Patrick Kolf, Jan Niklas Huber, Marco Boutros, Fadi Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on the image-level or on the representation-level. The representation-level morphing has been performed so far based on generative adversarial networks (GAN) where the encoded images are interpolated in the latent space to produce a morphed image based on the interpolated vector. Such a process was constrained by the limited reconstruction fidelity of GAN architectures. Recent advances in the diffusion autoencoder models have overcome the GAN limitations, leading to high reconstruction fidelity. This theoretically makes them a perfect candidate to perform representation-level face morphing. This work investigates using diffusion autoencoders to create face morphing attacks by comparing them to a wide range of image-level and representation-level morphs. Our vulnerability analyses on four state-of-the-art face recognition models have shown that such models are highly vulnerable to the created attacks, the MorDIFF, especially when compared to existing representation-level morphs. Detailed detectability analyses are also performed on the MorDIFF, showing that they are as challenging to detect as other morphing attacks created on the image- or representation-level. Data and morphing script are made public 1 Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition

Identity-driven Three-Player Generative Adversarial Network ...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Jan Niklas Kolf Tim Rieber Jurek Elliesen Fadi Boutros Arjan Kuijper Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available 1 .

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SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available 1 © 2023, CC BY-NC-SA.

关键词： Face recognition

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Ranking of attributes-comparative study based on data from stylometric domain 26

Ranking of attributes-comparative study based on data from s...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Zielosko, Beata Stanczyk, Urszula Zabinski, Krzysztof Institute of Computer Science University of Silesia in Katowice B edzinska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

关键词： Knowledge representation

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Minimum Coverage by Convex Polygons: The CG:SHOP Challenge 2023

arXiv

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arXiv 2023年

作者： Fekete, Sándor P. Keldenich, Phillip Krupke, Dominik Schirra, Stefan Department of Computer Science TU Braunschweig Germany Department for Simulation and Graphics OvGU Magdeburg Germany

We give an overview of the 2023 Computational Geometry Challenge targeting the problem Minimum Coverage by Convex Polygons, which consists of covering a given polygonal region (possibly with holes) by a minimum number of convex subsets, a problem with a long-standing tradition in Computational Geometry. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational geometry

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Deep Hierarchical Variational Autoencoders for World Models in Reinforcement Learning

Deep Hierarchical Variational Autoencoders for World Models ...

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First International Conference on Transdisciplinary AI (TransAI)

作者： Sriharshitha Ayyalasomayajula Banafsheh Rekabdar Christos Mousas Department of Computer Science Portland State University Portland OR USA Department of Computer Graphics Technology Purdue University West Lafayette IN USA

With the increasing demand for sample-efficient and robust reinforcement learning agents, particularly in intricate domains like robotics, healthcare, and gaming, there is a strong need to minimize the computational overhead caused by the interactions between real and virtual agents. This necessitates highly accurate models to simulate virtual agents and limit the number of such interactions. To this effect, model-based reinforcement learning (MBRL) has been proven very effective in formulating an environment with superior decision-making and higher learning efficiency. A known approach in MBRL is World Models, which uses a generative engine called Variational Autoencoders (VAE). VAE utilizes a relatively simple architecture constrained in processing power for complex image inputs. Therefore, the image reconstruction error is high. Recent research in VAEs has shown poor reconstruction quality. This paper proposes a Nouveau VAE (NVAE) based World Models to address the abovementioned limitations. NVAE, which employs deep convolutions in its architecture, is employed as the visual sensory component of the World Models and is used to encode the environment dynamics into a latent representation. We show that NVAE-based World Models perform exceptionally well in the dream environment of car racing-v2 (OpenAI GYM env), improving the agent's performance by 45%. We then demonstrate that the NVAE-based World Models can be applied to robotic simulation environments like panda-gym, where the agent achieved a 95 % success rate in solving the reach task.

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Clustering Single-Cell RNA-seq Data: Impact of Data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Data accuracy

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A Review of Satellite Image Analysis Tools 4th

A Review of Satellite Image Analysis Tools

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Department of Computer Science and Engineering Graphics Era Hill University Dehradun India Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta

ISBN: (纸本)9789819994410

The rapid growth of urban areas, population, and pollution presents a significant environmental challenge. Addressing these challenges requires innovative analytical approaches and data sources. This study focuses on the three-scale phenomena of the environmental system, physical environment, and regional surroundings, considering their implications on a global network scale. Remote sensing data, with its easy accessibility, offers new toolkits and methods to improve the global environment. It provides self-consistent measurements of vital physical attributes and surpasses traditional data sources regarding accuracy, efficiency, and coverage of larger geographical areas. This study aims to identify and analyze appropriate satellite image analysis tools for healthcare. The study objectives include studying available tools, analyzing their features, categorizing tools into open-source and commercial categories, and providing recommendations. This study emphasizes feature extraction and image enhancement parameters, which are crucial for image analysis. Further, this study provides a functional overview of various commercial and open-source satellite image analysis tools and suggests suitable tools for healthcare applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Geographic information systems

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Satellite Image Analysis in Health Care—A Systematic Review 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta Department of Computer Science Graphics Era Hill University Dehradun India

ISBN: (数字)9789819995219

ISBN: (纸本)9789819995202

Rapid urbanization, population expansion, and escalating pollution levels have given rise to novel environmental concerns that demand the application of creative, analytical methodologies and diverse data sources. To ensure efficient urban ecological management, it is crucial to consider three levels of phenomena: the environmental system, the physical environment, and the regional surroundings. Utilizing remote sensing data is a viable avenue for enhancing the global environment due to its convenient accessibility and ability to measure crucial physical features regularly. The primary objective of this research study is to ascertain and evaluate suitable methods for analyzing satellite imagery in the healthcare context. The study’s aims encompass examining the diverse array of tools accessible in the market, scrutinizing the characteristics of each device, exploring research papers that have employed these tools, and classifying the tools into open-source and commercial classifications. This study emphasizes the parameters associated with feature extraction and picture enhancement in satellite imagery, as these factors hold significant importance in the analysis of images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Remote sensing

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MIPGAN-Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity science 2021年第3期3卷 365-383页

作者： Zhang, Haoyu Venkatesh, Sushma Ramachandra, Raghavendra Raja, Kiran Damer, Naser Busch, Christoph Norwegian University of Science and Technology Gjovik2815 Norway Department of Igd Fraunhofer Institute for Computer Graphics Research Igd Darmstadt64283 Germany

Face morphing attacks target to circumvent Face Recognition Systems (FRS) by employing face images derived from multiple data subjects (e.g., accomplices and malicious actors). Morphed images can be verified against contributing data subjects with a reasonable success rate, given they have a high degree of facial resemblance. The success of morphing attacks is directly dependent on the quality of the generated morph images. We present a new approach for generating strong attacks extending our earlier framework for generating face morphs. We present a new approach using an Identity Prior Driven Generative Adversarial Network, which we refer to as MIPGAN (Morphing through Identity Prior driven GAN). The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed facial image with minimal artefacts and with high resolution. We demonstrate the proposed approach's applicability to generate strong morphing attacks by evaluating its vulnerability against both commercial and deep learning based Face Recognition System (FRS) and demonstrate the success rate of attacks. Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset. The obtained results demonstrate that the proposed approach of morph generation poses a high threat to FRS. © 2019 IEEE.

关键词： Face recognition

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