Refine
H-BRS Bibliography
- yes (93) (remove)
Departments, institutes and facilities
- Institut für Sicherheitsforschung (ISF) (93) (remove)
Document Type
- Article (43)
- Conference Object (34)
- Report (5)
- Doctoral Thesis (3)
- Patent (3)
- Contribution to a Periodical (2)
- Part of a Book (1)
- Conference Proceedings (1)
- Research Data (1)
Year of publication
Keywords
- Chemometrics (4)
- DNA typing (3)
- Raman spectroscopy (3)
- Classification (2)
- Cooperative Awareness Message (2)
- Discriminant analysis (2)
- Hyperspectral image (2)
- Intelligent Transport System (2)
- Principal Components Analysis (2)
- Privacy (2)
The detection of human skin in images is a very desirable feature for applications such as biometric face recognition, which is becoming more frequently used for, e.g., automated border or access control. However, distinguishing real skin from other materials based on imagery captured in the visual spectrum alone and in spite of varying skin types and lighting conditions can be dicult and unreliable. Therefore, spoofing attacks with facial disguises or masks are still a serious problem for state of the art face recognition algorithms. This dissertation presents a novel approach for reliable skin detection based on spectral remission properties in the short-wave infrared (SWIR) spectrum and proposes a cross-modal method that enhances existing solutions for face verification to ensure the authenticity of a face even in the presence of partial disguises or masks. Furthermore, it presents a reference design and the necessary building blocks for an active multispectral camera system that implements this approach, as well as an in-depth evaluation. The system acquires four-band multispectral images within T = 50ms. Using a machine-learning-based classifier, it achieves unprecedented skin detection accuracy, even in the presence of skin-like materials used for spoofing attacks. Paired with a commercial face recognition software, the system successfully rejected all evaluated attempts to counterfeit a foreign face.
A deployment of the Vehicle-2-Vehicle communication technology according to ETSI is in preparation in Europe. Currently, a policy for a necessary Public Key Infrastructure to enrol cryptographic keys and certificates for vehicles and infrastructure component is in discussion to enable an interoperable Vehicle-2-Vehicle communication. Vehicle-2-Vehicle communication means that vehicles periodically send Cooperative Awareness Messages. These messages contain the current geographic position, driving direction, speed, acceleration, and the current time of a vehicle. To protect privacy (location privacy, “speed privacy”) of vehicles and drivers ETSI provides a specific pseudonym concept. We show that the Vehicle-2-Vehicle communication can be misused by an attacker to plot a trace of sequent Cooperative Awareness Messages and to link this trace to a specific vehicle. Such a trace is non-disputable due to the cryptographic signing of the messages. So, the periodically sending of Cooperative Awareness Messages causes privacy problems even if the pseudonym concept is applied.
Die Detektion von Explosivstoffen stellt ein zentrales Feld der zivilen Sicherheitsforschung dar. Eine besondere Herausforderung liegt hierbei in dem Nachweis verpackter Substanzen, wie es bei Unkonventionellen Spreng- und Brandvorrichtung (USBV) häufig der Fall ist. Derzeit eingesetzte Verfahren arbeiten meist mit bildgebenden Techniken, durch die sich ein Anfangsverdacht ergibt. Der tatsächliche chemische Inhalt der USBV lässt sich jedoch nicht exakt ermitteln. Eine genaue Beurteilung der Gefährdung durch solche Substanzen ist allerdings von großer Bedeutung, insbesondere wenn die Entschärfung des Objekts in bewohntem Gebiet stattfinden muss. In der vorliegenden Arbeit wird ein Verfahren vorgestellt, das sich als Verifikationsverfahren bei bestehendem Anfangsverdacht gezielt einsetzen lässt. Hierzu wird mittels Laserbohrtechnik zunächst die äußere Hülle des zu untersuchenden Gegenstandes durchdrungen. Anschließend finden eine lasergestützte Probenahme des Inhalts sowie die Detektion unter Verwendung geeigneter Analysemöglichkeiten statt. Der Bohr- und Probenahmefortschritt wird über verschiedene spektroskopische und sensorische Verfahren begleitend überwacht. Zukünftig soll das System abstandsfähig auf Entschärfungsrobotern eingesetzt werden.
Der Asiatische Laubholzbockkäfer (Anoplophora glabripennis, kurz: ALB) ist ein Bockkäfer, der 2001 seinen Weg nach Europa fand. Er ist als Quarantäneschaderreger eingestuft und muss in Europa bekämpft werden. Eine der Möglichkeiten zum Aufspüren befallener Bäume ist der Einsatz von Spürhunden. Die Einstufung des ALB als Quarantäneschädling bringt große Probleme bei der Verwendung von Trainingsmaterial mit sich. Da es sich zudem um biologisches Material handelt, das geruchchemisch Änderungen und Variationen unterworfen ist, und da die für den Hund relevanten Geruchsstoffe nicht bekannt sind, ist es häufig schwierig, geeignete und frische Geruchsträger als Trainingshilfsmittel zur Verfügung zu stellen.
In the context of the Franco-German research project Re(h)strain, this work focuses on a global system analysis integrating both safety and security analysis of international and/or urban railway stations. The Re(h)strain project focuses on terrorist attacks on high speed train systems and investigates prevention and mitigation measures to reduce the overall vulnerability and strengthen the system resilience. One main criterion regarding public transport issues is the number of passengers. For example, the railway station of Paris “Gare du Nord” deals with a bigger number of passengers than the biggest airport in the world (SNCF open Data 2014), the Atlanta airport, but in terms of passengers, it is only around the 23rd rank railway station in the world. Due to the enormous mass of people, this leads to the system approach of breaking out the station into several classes of zones, e.g. entrance, main hall, quays, trains, etc. All classes are analysed considering state-of-the-art parameters, like targets attractiveness, feasibility of attack, possible damage, possible mitigation and defences. Then, safety incidence of security defence is discussed in order to refine security requirement with regard to the considered zone. Finally, global requirements of security defence correlated to the corresponding class of zones are proposed.
Entering the work envelope of an industrial robot can lead to severe injury from collisions with moving parts of the system. Conventional safety mechanisms therefore mostly restrict access to the robot using physical barriers such as walls and fences or non-contact protective devices including light curtains and laser scanners. As none of these mechanisms applies to human-robot-collaboration (HRC), a concept in which human and machine complement one another by working hand in hand, there is a rising need for safe and reliable detection of human body parts amidst background clutter. For this application camera-based systems are typically well suited. Still, safety concerns remain, owing to possible detection failures caused by environmental occlusion, extraneous light or other adverse imaging conditions. While ultrasonic proximity sensing can provide physical diversity to the system, it does not yet allow to reliably distinguish relevant objects from background objects.This work investigates a new approach to detecting relevant objects and human body parts based on acoustic holography. The approach is experimentally validated using a low-cost application-specific ultrasonic sensor system created from micro-electromechanical systems (MEMS). The presented results show that this system far outperforms conventional proximity sensors in terms of lateral imaging resolution and thus allows for more intelligent muting processes without compromising the safety of people working close to the robot. Based upon this work, a next step could be the development of a multimodal sensor systems to safeguard workers who collaborate with robots using the described ultrasonic sensor system.
The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.