Manuel Günther

University of Zurich

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Terrance E. Boult

University of Colorado Colorado Springs

Andras Rozsa

Verisk Analytics (United States)

Sébastien Marcel

Centre Hospitalier Métropole Savoie

Ethan M. Rudd

University of Colorado Colorado Springs

Rolf P. Würtz

Ruhr University Bochum

Akshay Raj Dhamija

University of Colorado Colorado Springs

Steve Cruz

University of Luxembourg

Laurent El Shafey

Google (United States)

Tiago de Freitas Pereira

Universidade Federal de Campina Grande

Rafael Henrique Vareto

Universidade Federal de Minas Gerais

Cooperative Institutions

University of Zurich

University of Colorado Colorado Springs

Ludwig-Maximilians-Universität München

Idiap Research Institute

Universidade Estadual de Campinas (UNICAMP)

Max Planck Institute of Psychiatry

LMU Klinikum

École Polytechnique Fédérale de Lausanne

Ruhr University Bochum

University of Bonn

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Refining Tuberculosis Detection in CXR Imaging: Addressing Bias in Deep Neural Networks via Interpretability

Özgür Güler Manuel Günther André Anjos

Interpretability

Refining (metallurgy)

Deep Neural Networks

10.1109/euvip61797.2024.10772829

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Citations (0)

Two Kinds of Statistics for Better Face Recognition

AIP conference proceedings (2010)

Manuel Günther Marco K. Müller Rolf P. Würtz Theodore E. Simos George Psihoyios

We briefly review the base techniques of elastic graph matching [1] and elastic bunch graph matching [2], which provide a method for face detection, matching, comparison, and identity decision. We then present a method that combines the advantages of Gabor‐labeled graphs with maximum likelihood decision making. The improvements over pure bunch graph matching have been studied, and the method has been successfully applied to large face databases like the one for the Face Recognition Grand Challenge [3]. Finally, we describe a system based on rank order statistics that can learn invariances in a minimally supervised way from a set of examples of individual faces in several situations like different head pose or illumination. Recognition rates are improved significantly without explicit modeling of the image formation process [4].

Rank (graph theory)

10.1063/1.3498288

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Citations (2)

Large-Scale Open-Set Classification Protocols for ImageNet

arXiv (Cornell University) (2022)

Jesus Andres Palechor Anacona Annesha Bhoumik Manuel Günther

Open-Set Classification (OSC) intends to adapt closed-set classification models to real-world scenarios, where the classifier must correctly label samples of known classes while rejecting previously unseen unknown samples. Only recently, research started to investigate on algorithms that are able to handle these unknown samples correctly. Some of these approaches address OSC by including into the training set negative samples that a classifier learns to reject, expecting that these data increase the robustness of the classifier on unknown classes. Most of these approaches are evaluated on small-scale and low-resolution image datasets like MNIST, SVHN or CIFAR, which makes it difficult to assess their applicability to the real world, and to compare them among each other. We propose three open-set protocols that provide rich datasets of natural images with different levels of similarity between known and unknown classes. The protocols consist of subsets of ImageNet classes selected to provide training and testing data closer to real-world scenarios. Additionally, we propose a new validation metric that can be employed to assess whether the training of deep learning models addresses both the classification of known samples and the rejection of unknown samples. We use the protocols to compare the performance of two baseline open-set algorithms to the standard SoftMax baseline and find that the algorithms work well on negative samples that have been seen during training, and partially on out-of-distribution detection tasks, but drop performance in the presence of samples from previously unseen unknown classes.

MNIST database

Softmax function

Training set

Robustness

Contextual image classification

10.48550/arxiv.2210.06789

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Citations (1)

Bi-modal biometric authentication on mobile phones in challenging conditions

Image and Vision Computing (2013)

Elie Khoury Laurent El Shafey Chris McCool Manuel Günther Sébastien Marcel

Mobile phone

Laptop

10.1016/j.imavis.2013.10.001

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Citations (37)

Learning and the Unknown: Surveying Steps toward Open World Recognition

Proceedings of the AAAI Conference on Artificial Intelligence (2019)

Terrance E. Boult S. Sánchez Cruz Akshay Raj Dhamija Manuel Günther James Henrydoss

As science attempts to close the gap between man and machine by building systems capable of learning, we must embrace the importance of the unknown. The ability to differentiate between known and unknown can be considered a critical element of any intelligent self-learning system. The ability to reject uncertain inputs has a very long history in machine learning, as does including a background or garbage class to account for inputs that are not of interest. This paper explains why neither of these is genuinely sufficient for handling unknown inputs – uncertain is not unknown, and unknowns need not appear to be uncertain to a learning system. The past decade has seen the formalization and development of many open set algorithms, which provably bound the risk from unknown classes. We summarize the state of the art, core ideas, and results and explain why, despite the efforts to date, the current techniques are genuinely insufficient for handling unknown inputs, especially for deep networks.

10.1609/aaai.v33i01.33019801

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Citations (110)

Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen

Physics and Imaging in Radiation Oncology (2023)

Riccardo Dal Bello Mariia Lapaeva Agustina La Greca Saint-Esteven Philipp Wallimann Manuel Günther

The superior tissue contrast of magnetic resonance (MR) compared to computed tomography (CT) led to an increasing interest towards MR-only radiotherapy. For the latter, the dose calculation should be performed on a synthetic CT (sCT). Patient-specific quality assurance (PSQA) methods have not been established yet and this study aimed to assess several software-based solutions.A retrospective study was performed on 20 patients treated at an MR-Linac, which were selected to evenly cover four subcategories: (i) standard, (ii) air pockets, (iii) lung and (iv) implant cases. The neural network (NN) CycleGAN was adopted to generate a reference sCT, which was then compared to four PSQA methods: (A) water override of body, (B) five tissue classes with bulk densities, (C) sCT generated by a separate NN (pix2pix) and (D) deformed CT.The evaluation of the dose endpoints demonstrated that while all methods A-D provided statistically equivalent results (p = 0.05) within the 2% level for the standard cases (i), only the methods C-D guaranteed the same result over the whole cohort. The bulk densities override was shown to be a valuable method in absence of lung tissue within the beam path.The observations of this study suggested that the use of an additional sCT generated by a separate NN was an appropriate tool to perform PSQA of a sCT in an MR-only workflow at an MR-Linac. The time and dose endpoints requirements were respected, namely within 10 min and 2%.

10.1016/j.phro.2023.100464

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Citations (7)

Watchlist Challenge: 3rd Open-set Face Detection and Identification

arXiv (Cornell University) (2024)

Farizul Hafiz Kasim Terrance E. Boult Rensso Mora Bernardo Biesseck Rafael Oliveira Ribeiro

In the current landscape of biometrics and surveillance, the ability to accurately recognize faces in uncontrolled settings is paramount. The Watchlist Challenge addresses this critical need by focusing on face detection and open-set identification in real-world surveillance scenarios. This paper presents a comprehensive evaluation of participating algorithms, using the enhanced UnConstrained College Students (UCCS) dataset with new evaluation protocols. In total, four participants submitted four face detection and nine open-set face recognition systems. The evaluation demonstrates that while detection capabilities are generally robust, closed-set identification performance varies significantly, with models pre-trained on large-scale datasets showing superior performance. However, open-set scenarios require further improvement, especially at higher true positive identification rates, i.e., lower thresholds.

Identification

10.48550/arxiv.2409.07220

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Citations (0)

Computerrollenspiele

transcript Verlag eBooks (2023)

Manuel Günther

Eine der wirtschaftlich erfolgreichsten Gattungen von Computerspielen ist die der Computerrollenspiele, in denen definierte Charaktere heroisch gegen das Übel der Welt hinausziehen.Mit dieser wirtschaftlichen Bedeutsamkeit kommt auch das Phänomen prekärer Arbeitsbedingungen in der Herstellung, für das die ganze Branche mittlerweile bekannt ist.Da Computerrollenspiele allerdings auch eine der ältesten Computerspielgattungen sind, reicht ihre Geschichte noch vor jede Ausbeutung in der Gamesindustrie zurück.Darum lässt sich zeigen, dass die Vermarktung zwar eine vermehrte Produktion und Verbreitung dieser Sorte Unterhaltungssoftware, zugleich aber auch eine Verkehrung einer ihrer Hauptfiguren, nämlich des*der Gamedesigner*in, bewirkt hat.Ohne an dieser Stelle ein weiteres Mal darauf zurückzugreifen, dass die Entstehung von Computerspielen überhaupt »ausgedientes Heeresgerät zum Missbrauch« 2 benötigt, soll dafür betrachtet werden, wie die ursprünglichen Exemplare dessen, was heute englischsprachig computer role-playing game oder kurz CRPG heißt, hergestellt, bedroht und verteidigt wurden.Nach einer kurzen theoretischen Erklärung,

10.1515/9783839467909-002

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A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions

arXiv (Cornell University) (2016)

Ethan M. Rudd Andras Rozsa Manuel Günther Terrance E. Boult

As our professional, social, and financial existences become increasingly digitized and as our government, healthcare, and military infrastructures rely more on computer technologies, they present larger and more lucrative targets for malware. Stealth malware in particular poses an increased threat because it is specifically designed to evade detection mechanisms, spreading dormant, in the wild for extended periods of time, gathering sensitive information or positioning itself for a high-impact zero-day attack. Policing the growing attack surface requires the development of efficient anti-malware solutions with improved generalization to detect novel types of malware and resolve these occurrences with as little burden on human experts as possible. In this paper, we survey malicious stealth technologies as well as existing solutions for detecting and categorizing these countermeasures autonomously. While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution. The most notable of these flawed assumptions is the closed world assumption: that no sample belonging to a class outside of a static training set will appear at query time. We present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains.

Attack surface

10.48550/arxiv.1603.06028

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Citations (1)

PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses

arXiv (Cornell University) (2016)

Ethan M. Rudd Manuel Günther Terrance E. Boult

For applications such as airport border control, biometric technologies that can process many capture subjects quickly, efficiently, with weak supervision, and with minimal discomfort are desirable. Facial recognition is particularly appealing because it is minimally invasive yet offers relatively good recognition performance. Unfortunately, the combination of weak supervision and minimal invasiveness makes even highly accurate facial recognition systems susceptible to spoofing via presentation attacks. Thus, there is great demand for an effective and low cost system capable of rejecting such attacks.To this end we introduce PARAPH -- a novel hardware extension that exploits different measurements of light polarization to yield an image space in which presentation media are readily discernible from Bona Fide facial characteristics. The PARAPH system is inexpensive with an added cost of less than 10 US dollars. The system makes two polarization measurements in rapid succession, allowing them to be approximately pixel-aligned, with a frame rate limited by the camera, not the system. There are no moving parts above the molecular level, due to the efficient use of twisted nematic liquid crystals. We present evaluation images using three presentation attack media next to an actual face -- high quality photos on glossy and matte paper and a video of the face on an LCD. In each case, the actual face in the image generated by PARAPH is structurally discernible from the presentations, which appear either as noise (print attacks) or saturated images (replay attacks).

Presentation (obstetrics)

Replay attack

Spoofing attack

10.48550/arxiv.1605.03124

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Citations (1)