Mario Hildebrandt

Privacy preserving challenges: new design aspects for latent fingerprint detection systems with contact-less sensors for future preventive applications in airport luggage handling

This paper provides first ideas and considerations for designing and developing future technologies relevant for challenging privacy-preserving preventive applications of contact-less sensors. We introduce four use-cases: preventive detailed acquisition of fingerprints, coarse scans for fingerprint localisation, separation of overlapping fingerprints and age determination for manipulation detection and automatic securing of evidence. To enable and support these four use-cases in future, we suggest developing four techniques: coarse scans, detailed scans, separation and age determination of fingerprints. We derive a new definition for the separation from a forensic approach: presence detection of overlapping fingerprints, estimation of the number of fingerprints, separation and sequence (order) detection. We discuss main challenges for technical solutions enabling the suggested privacy-preserving use-cases combined with a brief summary of preliminary results from our first experiments. We analyse the legal principles and requirements for European law and the design of the use-cases, which show tendencies for other countries.

Benchmarking contact-less surface measurement devices for fingerprint acquisition in forensic investigations: Results for a differential scan approach with a chromatic white light sensor

With the advent of new contact-less sensors for forensic investigations of latent fingerprint traces, the authors see the need for a benchmarking framework to evaluate existing devices and promising combinations of data acquisition and signal processing techniques. This paper extends the existing benchmarking framework from [1] by categorizing it into properties from a forensic point-of-view (end-user) and a technical point-of-view (scientific-user) and applies a known differential image technique for the subjective evaluation of which traces are visible. We show exemplary results for a chromatic white light (CWL) sensor for the surface quality assessment, using and comparing the experimental setup of 10 surfaces from [1] and additional 10 surfaces, including real-world objects, to determine its potential for detecting latent fingerprints. Using a differential image approach, the particular influence of sensor noise signals is analyzed, showing that this differential approach cannot always be considered as an ideal filter for fingerprint pattern detection.

Printed fingerprints: a framework and first results towards detection of artificially printed latent fingerprints for forensics

In Schwarz 1 an amino acid model for printing latent fingerprints to porous surfaces is introduced, motivated by the need for reproducibility tests of different development techniques for forensic investigations. However, this technique also enables the fabrication of artificial traces constituting a possible threat to security, motivating a need for research of appropriate detection techniques. In this paper a new framework for modelling the properties of a generic fingerprint examination process is introduced. Based on the framework, examination properties and detection properties are derived by a subjective evaluation. We suggest a first formalisation of exemplary properties, which can be easily extended to fit different needs. We present a first experimental setup limited to two printers and the Schwarz amino acid model using absorbing and non-absorbing material with first results to show tendencies and underline the necessity for further research.

StirTraceV2.0: Enhanced Benchmarking and Tuning of Printed Fingerprint Detection

In this paper, we address the problem of assessing the overall quality of forgery detection approaches for artificial sweat printed latent fingerprints placed at crime scenes. It is very important to have reliable detection mechanisms tested on manifold characteristics caused for example by different surfaces, printers and during acquisition, avoiding misleading crime scene investigations. Today only a limited number of detection methods exist in the literature and test sets are still limited in size and quality covering all different conditions (influence factors). Based on the recently introduced publicly available StirTrace tool, we enhance the functionality to simulate complex and realistic test sets and discuss how detection approaches can be tuned by further preprocessing and feature selection. Our contributions here are twofold. First, we suggest a benchmarking design in 16-bit domain working in full bit-depth of todays nanometer sensory and propose enhancements for further simulations of sensor and substrate characteristics as well as single and combined scan artifacts (simulated, novel experimental data set of in sum 1.254.000 samples). Second, we benchmark exemplarily two known feature sets on nonsimulated and simulated data and compare findings with additional preprocessing and feature selection. Finally, we summarize lessons learned how good todays detection works and which challenges exist for achieving a high reliability. For the community we provide a tool, which can be used as fundamental basis to simulate influence factors allowing a systematic comparison and benchmarking of results. We also want to motivate further research in the design and tuning of forgery detection approaches.

Advanced techniques for latent fingerprint detection and validation using a CWL device

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.

Convergence of digital and traditional forensic disciplines: a first exemplary study for digital dactyloscopy

Traditional forensic disciplines get increasingly digitized. This allows for new investigations and often provides more details for each trace. Due to this digitalization, digital and digitized forensic disciplines have very similar courses of action and requirements. Therefore, we introduce a generic process model, which can be easily adapted for different purposes. Here, we show an exemplary study for the digital dactyloscopy as part of digitized forensics. The digitalization effort can potentially allow for different investigations of the very same trace, is possible because the original trace is acquired non-destructively.

A Common Scheme for Evaluation of Forensic Software

We introduce a first common evaluation scheme for forensic software. Therefore, we investigate potential attacks on forensic software to derive preliminary attacker models. We use the Federal Rules of Evidence and the Daubert Challenge of the US jurisdiction to investigate the legal fundamentals for forensic software and to show tendencies for other countries. Furthermore, current approaches for the validation and verification of forensic software are summarized. Subsequently, our proposed evaluation scheme is used for the exemplary evaluation of the forensic duplication application dcfldd and the forensic toolkit EnCase Forensic. Furthermore, it is used to create a preliminary framework for the development of forensic software. The formalization of our evaluation scheme classifies the forensic application according to the model of the forensic process of Kiltz et al. This scheme is intended to be extensible and to support the benchmarking of forensic applications.

Context analysis of artificial sweat printed fingerprint forgeries: Assessment of properties for forgery detection

In our paper we investigate the attack based on means of artificial sweat printed fingerprint forgeries at crime scenes by studying the attack chain in general and derive potential context properties from the attack, which help to describe the attack more precisely. Based on the attack chain context properties, potential detection and context anomaly properties are derived and suggested as an enabler for proper detection of such forgeries for forensic experts during forensic investigation and interpretation of traces. It is a first study for the discussion with the community to motivate further work addressing open issues in this domain of crime scene forgery detection. Potential means known from biometric analysis as well as from media forensics are included to enhance forensic trace interpretation. In summary, our first systematic study includes a four-step attack chain, 14 main attack context properties (to describe the attack) and 15 correspondingly derived detection and anomaly properties for the forensic expert usage. A first simulation of the application for two exemplary attacks (A and B) shows, how our defined properties can be used, from two viewpoints: which one of properties has the particular attack and which properties might help to identify the trace as a artificial sweat printed fingerprint forgery during investigation of the trace during forensic interpretation (without prior knowing the existence of the attack).

High-resolution printed amino acid traces: a first-feature extraction approach for fingerprint forgery detection

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.

Visibility enhancement and validation of segmented latent fingerprints in crime scene forensics

Forensic investigators are permanently looking for novel technologies for fast and effective recovering of latent fingerprints at a crime scene. Traditionally, this work is done manually and therefore considered very time consuming. Highly skilled experts apply chemical reagents to improve visibility of traces and use digital cameras or adhesive tape to lift prints. Through an automation of the surface examination, larger areas can be investigated faster. This work amplifies the experimental study on capabilities of a chromatic white-light sensor (CWL) regarding the contact-less lifting of latent fingerprints from differently challenging substrates. The crucial advantage of a CWL sensor compared to taking digital photographs is the simultaneous acquisition of luminance and topography of the surface, extending the standard twodimensional image processing to the analysis of three-dimensional data. The paper focuses on the automatic validation of localized fingerprint regions. In contrast to statistical features from luminance data, previously used for localization, we propose the streakiness of a pattern as the basic feature indicating the fingerprint presence. Regions are analyzed for streakiness using both luminance and topography data. As a result, the human experts significantly save time by dealing with a limited number of approved fingerprints. The experiments show that the validation performance in terms of equal error rate does not exceed 6% even on very challenging substrates regarding high-quality fingerprints.