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This Paper proposes a novel method for the blind detection of image pre-processing techniques by means of statistical pattern recognition in image forensics. The technique is intended to detect sensor intrinsic pre-processing steps as well as manually applied filters. We have exemplary chosen 6 pre-processing filters with different parameter settings. The concept utilizes 29 image features which are supposed to allow for a reliable model creation during supervised learning. The evaluation of the trained models indicates average accuracies between 82.50 and 94.53%. The investigation of image data from 8 sensors leads to the detection of credible pre-processing filters. Those results adumbrate that our method might be suitable to prove the authenticity of the data origin and the integrity of image data based on the detected preprocessing techniques. The preliminary evaluation for manually applied filters yields recognition accuracies between 39.09% (14 classes) and 53.33% (7 classes).
Since the face morphing attack was introduced by Ferrara et al. in 2014, the detection of face morphings has become a wide spread topic in image forensics. By now, the community is very active and has reported diverse detection approaches. So far, the evaluations are mostly performed on images without post-processing. Face images stored within electronic machine readable documents (eMRTD) are ICAO-passport-scaled to a resolution of 413x531 and a JPG or JP2 lesize of 15 kilobytes. This paper introduces a face morphing detection concept with 3 modules (ICAO-aligned pre- processing module, feature extraction module and classi cation module), tailored for such images on eMRTD. In this work we exemplary design and evaluate two feature spaces for the feature extraction module, a frequency domain and a spatial domain feature space. Our evaluation will compare both feature spaces and is carried out with 66,229 passport-scaled images (64,363 morphed face images and 1,866 authentic face images) which are completly independent from training and include all images provided for the IHMMSEC'19 special session: "Media Forensics - Fake or Real?". Furthermore, we investigate the in uence of di erent morph gen- eration pipelines to the detection accuracies of the concept and we analyse the impact of neutral and smiling genuine faces to the morph detector performance. The evaluation determines a detection rate of 86.0% for passport-scaled morphed images with a false alarm rate of 4.4% for genuine images for the spatial domain feature space.
Reducing the False Alarm Rate for Face Morph Detection by a Morph Pipeline Footprint Detector
(2018)
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.
We analyze StirTrace towards benchmarking face morphing forgeries and extending it by additional scaling functions for the face biometrics scenario. We benchmark a Benford's law based multi-compression-anomaly detection approach and acceptance rates of morphs for a face matcher to determine the impact of the processing on the quality of the forgeries. We use 2 different approaches for automatically creating 3940 images of morphed faces. Based on this data set, 86614 images are created using StirTrace. A manual selection of 183 high quality morphs is used to derive tendencies based on the subjective forgery quality. Our results show that the anomaly detection seems to be able to detect anomalies in the morphing regions, the multi-compression-anomaly detection performance after the processing can be differentiated into good (e.g. cropping), partially critical (e.g. rotation) and critical results (e.g. additive noise). The influence of the processing on the biometric matcher is marginal.
Towards a Protection Profile for User-Centric andSelf-Determined Privacy Management in Biometrics
(2018)
First investigation of latent fingerprints long-term aging using chromatic white light sensors
(2013)
Visibility Assessment of Latent Fingerprints on Challenging Substrates in Spectroscopic Scans
(2013)
Visibility enhancement and validation of segmented latent fingerprints in crime scene forensics
(2013)
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.
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