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The technology-aided support of forensic experts while investigating crime scenes and collecting traces becomes a more and more important part in the domains of image acquisition and signal processing. The manual lifting of latent fingerprints using conventional methods like the use of carbon black powder is time-consuming and very limited in its scope of application. New technologies for a contact-less and non-invasive acquisition and automatic processing of latent fingerprints, promise the possibilities to inspect much more and larger surface areas and can significantly simplify and speed up the workflow. Furthermore, it allows multiple investigations of the same trace, subsequent chemical analysis of the residue left behind and the acquisition of latent fingerprints on sensitive surfaces without destroying the surface itself. In this work, a FRT MicroProf200 surface measurement device equipped with a chromatic white-light sensor CWL600 is used. The device provides a gray-scale intensity image and 3D-topography data simultaneously. While large area scans are time-consuming, the detection and localization of finger traces are done based on low-resolution scans. The localized areas are scanned again with higher resolution. Due to the broad variety of different surface characteristics the fingerprint pattern is often overlaid by the surface structure or texture. Thus, image processing and classification techniques are proposed for validation and visualization of ridge lines in high-resolution scans. Positively validated regions containing complete or sufficient partial fingerprints are passed on to forensic experts. The experiments are provided on a set of three surfaces with different reflection and texture characteristics, and fingerprints from ten different persons.

Determining the age of latent fingerprint traces found at crime scenes is an unresolved research issue since decades. Solving this issue could provide criminal investigators with the specific time a fingerprint trace was left on a surface, and therefore would enable them to link potential suspects to the time a crime took place as well as to reconstruct the sequence of events or eliminate irrelevant fingerprints to ensure privacy constraints. Transferring imaging techniques from different application areas, such as 3D image acquisition, surface measurement and chemical analysis to the domain of lifting latent biometric fingerprint traces is an upcoming trend in forensics. Such non-destructive sensor devices might help to solve the challenge of determining the age of a latent fingerprint trace, since it provides the opportunity to create time series and process them using pattern recognition techniques and statistical methods on digitized 2D, 3D and chemical data, rather than classical, contact-based capturing techniques, which alter the fingerprint trace and therefore make continuous scans impossible. In prior work, we have suggested to use a feature called binary pixel, which is a novel approach in the working field of fingerprint age determination. The feature uses a Chromatic White Light (CWL) image sensor to continuously scan a fingerprint trace over time and retrieves a characteristic logarithmic aging tendency for 2D-intensity as well as 3D-topographic images from the sensor. In this paper, we propose to combine such two characteristic aging features with other 2D and 3D features from the domains of surface measurement, microscopy, photography and spectroscopy, to achieve an increase in accuracy and reliability of a potential future age determination scheme. Discussing the feasibility of such variety of sensor devices and possible aging features, we propose a general fusion approach, which might combine promising features to a joint age determination scheme in future. We furthermore demonstrate the feasibility of the introduced approach by exemplary fusing the binary pixel features based on 2D-intensity and 3D-topographic images of the mentioned CWL sensor. We conclude that a formula based age determination approach requires very precise image data, which cannot be achieved at the moment, whereas a machine learning based classification approach seems to be feasible, if an adequate amount of features can be provided.

This article describes primarily the development and empiric validation of a design for security warning messages on smartphones for primary school children (7-10 years old). Our design approach for security warnings for children uses a specific character and is based on recommendations of a paediatrician expert. The design criteria are adapted to children's skills, e.g. their visual, acoustic, and haptic perception and their literacy. The developed security warnings are prototypically implemented in an iOS application (on the iPhone 3G/4G) where children are warned by a simulated anti-malware background service, while they are busy with another task. For the evaluation we select methods for empiric validation of the design approach from the field of usability testing ("think aloud" test, questionnaires, log-files, etc.). Our security warnings prototype is evaluated in an empiric user study with 13 primary school children, aged between 8 and 9 years and of different gender (5 girls, 8 boys). The evaluation analysis shows, that nearly all children liked the design of our security warnings. Surprisingly, on several security warning messages most of the children react in the right way after reading the warning, although the meaning couldn't be interpreted in the right way. Another interesting result is, that several children relate specific information, e.g. update, to a specific character. Furthermore, it could be seen that most of the primary school test candidates have little awareness of security threats on smartphones. It is a very strong argument to develop e.g. tutorials or websites in order to raise awareness and teach children how to recognize security threats and how to react to them. Our design approach of security warnings for children's smartphones can be a basis for warning on other systems or applications like tutorials, which are used by children. In a second investigation, we focus on webpages, designed for children since smartphones and webpages (the services behind) are more and more interconnected. From this point of view those services should continue the securityapproaches for children's smartphones. The webservices were evaluated among different criteria, e.g. data protection. The results of a first investigation are reported in this paper.

In this paper, we extend an existing context model for statistical pattern recognition based microphone forensics by: first, generating a generalized model for this process and second, using this general model to construct a complex new application scenario model for microphone forensic investigations on the detection of playback recordings (a.k.a. replays, re-recordings, double-recordings). Thereby, we build the theoretical basis for answering the question whether an audio recording was made to record a playback or natural sound. The results of our investigations on the research question of playback detection imply that it is possible with our approach on our evaluation set of six microphones. If the recorded sound is not modified prior to playback, we achieve in our tests 89.00% positive indications on the correct two microphones involved. If the sound is post-processed (here, by normalization) this figure decreases (in our normalization example to 36.00%, while another 50.67% of the tests still indicate two microphones, of which one has actually not been involved in the recording and playback recording process).

Fingerprints are used for the identification of individuals for over a century in crime scene forensics. Here, often physical or chemical preprocessing techniques are used to render a latent fingerprint visible. For quality assurance purposes of those development techniques, Schwarz1 introduces a technique for the reproducible generation of latent fingerprints using ink-jet printers and artificial amino acid sweat. However, this technique allows for printing latent fingerprints at crime scenes to leave false traces, too. Hence, Kiltz et al.2 introduce a first framework for the detection of printed fingerprints. However, the utilized printers have a maximum resolution of 2400×1200 dpi. In this paper, we use a Canon PIXMA iP46003 printer with a much higher resolution of 9600×400 dpi, which does not produce the kind of visible dot patterns reported in Kiltz et al.2 We show that an acquisition with a resolution of 12700 to 25400 ppi is necessary to extract microstuctures, which perspectively allows for an automated detection of printed fingerprint traces fabricated with high-resolution printers. Using our first test set with 20 printed and 20 real, natural fingerprint patterns from the human the evaluation results indicate a very positive tendency towards the detectability of such traces using the method proposed in this paper.

In this article we illustrate an approach of a security threat analysis of the quadrocopter AR.Drone, a toy for augmented reality (AR) games. The technical properties of the drone can be misused for attacks, which may relate security and/or privacy aspects. Our aim is to sensitize for the possibility of misuses and the motivation for an implementation of improved security mechanisms of the quadrocopter. We focus primarily on obvious security vulnerabilities (e.g. communication over unencrypted WLAN, usage of UDP, live video streaming via unencrypted WLAN to the control device) of this quadrocopter. We could practically verify in three exemplary scenarios that this can be misused by unauthorized persons for several attacks: high-jacking of the drone, eavesdropping of the AR.Drones unprotected video streams, and the tracking of persons. Amongst other aspects, our current research focuses on the realization of the attack of tracking persons and objects with the drone. Besides the realization of attacks, we want to evaluate the potential of this particular drone for a "safe-landing" function, as well as potential security enhancements. Additionally, in future we plan to investigate an automatic tracking of persons or objects without the need of human interactions.

In an attempt to further develop and evaluate the optical recognition systems for distinguishing between driver and frontseat passenger during their interactions with dual-view touch screen integrated to the automobile centre console, this work focuses on the enhancement of both image processing algorithms and experimental environment. In addition to the motion based forearm and hand segmentation and the texture based arm direction analysis, the adaptive boosting classifiers with Haar-like features have been engaged for the learning of driver's and passenger's hand patterns. The user discrimination system was completely reproduced in a laboratory, including passenger compartment with genuine dashboard, touch screen, camera and near-infrared lamps, so that different illumination conditions could be modeled. The new acquisition system allows automatic and unambiguous registration of all touch screen interactions and their synchronization with the video stream. This results in credible evaluation of the image processing routines. The adjustment of the camera position and the active infrared illumination made it possible to reduce the recognition error rates and to achieve superior discrimination performance compared to previous works.