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In forensic investigations, the recovering of latent fingerprints is one of the most essential issues. Driven by human experts, today this process is very time consuming. An automation of both examination of suspicious areas and acquisition of fingerprints lead on the one hand to the covering of larger surfaces and on the other hand to significant speed up of the evidence collection. This work presents an experimental study on capabilities of chromatic white-light sensor (CWL) regarding the contact-less localization of latent fingerprints on differently challenging substrates. The fully automatic CWL-based system is implemented from the acquisition through the feature extraction right up to the classification. The key objective of the work is to develop a methodological approach for the quantitative evaluation of the localization success. Based on the proposed performance measures, the optimal system parameters such as scan resolution, extracted features and classification scheme are specified dependent on the surface material. Our experiments from an actual project with the sensor industry partner show convincing localization performance on easy-to-localize and adequate performance on moderate-to-localize substrates. The hard-to-localize substrates require further improvements of the localization system.
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
Visibility enhancement and validation of segmented latent fingerprints in crime scene forensics
(2013)
Visibility Assessment of Latent Fingerprints on Challenging Substrates in Spectroscopic Scans
(2013)
First investigation of latent fingerprints long-term aging using chromatic white light sensors
(2013)
Towards a Protection Profile for User-Centric andSelf-Determined Privacy Management in Biometrics
(2018)
Due to the increasing number of complaints alleging privacy violations against companies to data protection authorities, the translation of business goals to system design goals and the subsequent consequences for customers’ privacy poses a challenge for many companies. For this reason, there is a need to bridge the economics of privacy and threats to privacy. To this end, our work relies on the concept of privacy as contextual integrity. This framework defines privacy as appropriate information flows subjected to social norms within particular social contexts or spheres. In this paper, we introduce a preliminary version of a semantic model which aims to relate and provide understanding on how well-established business goals may affect their customers’ privacy by designing IoT devices with permission access, data acquired by sensors, among other factors. Finally, we provide a use case application showing how to use the semantic model. The model aims to be an educational tool for professionals in business informatics during the modeling and designing process of a product which may gather sensitive data or may infer sensitive information, giving an understanding of the interaction of the product and its footprint with diverse actors (humans or machines). In the future, a further complete model of the presented may also target other groups, such as law enforcement bodies, as part of their educational training in such systems.
Steganography embraces several hiding techniques which spawn across multiple domains. However, the related terminology is not unified among the different domains, such as digital media steganography, text steganography, cyber-physical systems steganography, network steganography (network covert channels), local covert channels, and out-of-band covert channels. To cope with this, a prime attempt has been done in 2015, with the introduction of the so-called hiding patterns, which allow to describe hiding techniques in a more abstract manner. Despite significant enhancements, the main limitation of such a taxonomy is that it only considers the case of network steganography. Therefore, this paper reviews both the terminology and the taxonomy of hiding patterns as to make them more general. Specifically, hiding patterns are split into those that describe the embedding and the representation of hidden data within the cover object. As a first research action, we focus on embedding hiding patterns and we show how they can be applied to multiple domains of steganography instead of being limited to the network scenario. Additionally, we exemplify representation patterns using network steganography.
Steganographic and covert communication is increasingly used for hiding attacks. Often information hiding is used by attackers in advanced persistent threats in order to operate without being noticed. Attackers might use it to hide data exfiltration or control channels for persistent malware. The paper analyzes potential threats to operational technology by investigating different communication protocols towards their suitability as cover channels. Using a generalized network architecture model and the communication flows of nuclear power plants, the attack potential using information hiding is exemplary assessed. An example for a supply chain attack for command injection is given on the foundation of the OPC UA protocol and an off-theshelf programmable logic controller. Subsequently, recommendations for a strategic and operational preparation for operators towards the prevention and detection of information hiding attacks are derived.
Since its introduction in 2014, the face morphing forgery (FMF) attack has received significant attention from the biometric and media forensic research communities. The attack aims at creating artificially weakened templates which can be successfully matched against multiple persons. If successful, the attack has an immense impact on many biometric authentication scenarios including the application of electronic machine-readable travel document (eMRTD) at automated border control gates. We extend the StirTrace framework for benchmarking FMF attacks by adding five issues: a novel three-fold definition for the quality of morphed images, a novel FMF realisation (combined morphing), a post-processing operation to simulate the digital image format used in eMRTD (passport scaling 15 kB), an automated face recognition system (VGG face descriptor) as additional means for biometric quality assessment and two feature spaces for FMF detection (keypoint features and fusion of keypoint and Benford features) as additional means for forensic quality assessment. We show that the impact of StirTrace post-processing operations on the biometric quality of morphed face images is negligible except for two noise operators and passport scaling 15 kB, the impact on the forensic quality depends on the type of post-processing, and the new FMF realisation outperforms the previously considered ones.
This paper introduces an approach to automatic generation of visually faultless facial morphs along with a proposal on how such morphs can be automatically detected. It is endeavored that the created morphs cannot be recognized as such with the naked eye and a reference automatic face recognition (AFR) system produces high similarity scores while matching a morph against faces of persons who participated in morphing. Automatic generation of morphs allows for creating abundant experimental data, which is essential (i) for evaluating the performance of AFR systems to reject morphs and (ii) for training forensic systems to detect morphs. Our first experiment shows that human performance to distinguish between morphed and genuine face images is close to random guessing. In our second experiment, the reference AFR system has verified 11.78% of morphs against any of genuine images at the decision threshold of 1% false acceptance rate. These results indicate that facial morphing is a serious threat to access control systems aided by AFR and establish the need for morph detection approaches. Our third experiment shows that the distribution of Benford features extracted from quantized DCT coefficients of JPEG-compressed morphs is substantially different from that of genuine images enabling the automatic detection of morphs.