Two different approaches for constructing a steganographic channel in an IEEE 802.11 (WLAN) network are introduced in this paper. First test results on the reliability, undetectability and capacity of a prototypical implementation of both approaches are described and discussed.

The datasets of synthetic biometric samples are created having in mind two major objectives: bypassing privacy concerns and compensating for missing sample variability in datasets of real biometric samples. If the purpose of generating samples is the evaluation of biometric systems, the foremost challenge is to generate so-called mated impressions—different fingerprints of the same finger. Note that for fingerprints, the finger’s identity is given by the co-location of minutiae points. The other challenge is to ensure the realism of generated samples. We solve both challenges by reconstructing fingerprints from pseudo-random minutiae making use of the pix2pix network. For controlling the identity of mated impressions, we derive the locations and orientations of minutiae from randomly created non-realistic synthetic fingerprints and slightly modify them in an identity-preserving way. Our previously trained pix2pix models reconstruct fingerprint images from minutiae maps, ensuring that the realistic appearance is transferred from training to synthetic samples. The main contribution of this work lies in creating and making public two synthetic fingerprint datasets of 500 virtual subjects with 8 fingers each and 10 impressions per finger, totaling 40,000 samples in each dataset. Our synthetic datasets are designed to possess characteristics of real biometric datasets. Thus, we believe they can be applied for the privacy-friendly testing of fingerprint recognition systems. In our evaluation, we use NFIQ2 for approving the visual quality and Verifinger SDK for measuring the reconstruction success.

The fusion of biometric systems, algorithms and/or traits is a well known solution to improve authentication performance of biometric systems. In this article the fusion of two instances of the same semantic is suggested, where semantics are alternative handwritten contents such as numbers or sentences, in addition to commonly used signature. In order to fuse two instances of one semantic, a biometric authentication is carried out on both by Biometric Hash algorithm up to matching score computation. The fusion is done by combination of matching scores to a joint score as basis for authentication decision. Three individual fusion strategies are used to study to which degree the authentication performance can be improved or degraded. Therefore one pragmatic and two optimistically weighting approaches for biometric fusion are used. The best fusion result is even better than the corresponding best individual result by approximately 17%.

Academic research in media forensics mainly focuses on methods for the detection of the traces or artefacts left by media manipulations in media objects. While the resulting detectors often achieve quite impressive detection performances, when tested under lab conditions, hardly any of those have yet come close to the ultimate benchmark for any forensic method, which would be courtroom readiness. This paper tries first to facilitate the different stakeholder perspectives in this field and then to partly address the apparent gap between the academic research community and the requirements imposed onto forensic practitioners. The intention is to facilitate the mutual understanding of these two classes of stakeholders and assist with first steps intended at closing this gap. To do so, first a concept for modelling media forensic investigation pipelines is derived from established guidelines. Then, the applicability of such modelling is illustrated on the example of a fusion-based media forensic investigation pipeline aimed at the detection of DeepFake videos using five exemplary detectors (hand-crafted, in one case neural network supported) and testing two different fusion operators. At the end of the paper, the benefits of such a planned realisation of AI-based investigation methods are discussed and generalising effects are mapped out.

A stroke point matching by dynamic time warping (DTW) and strokes wise segment alignment by using spline interpolation for time and coordinate signals are presented in this paper. The algorithm uses both the features and signals based verification methodologies. The top-level in signature representation deals with the global geometric shape. This method will verify not only the signature shape but also some of its important features. In the first stage, we verify the signature based on the signature feature. In the second stage, we will verify time profile and coordinate signals of the signature by new segment warping functional approach. Local temporal shifts existing in signature can be reduced through segment warping technique. The decision about the authenticity of the specimen is influenced by correlation measurements and statistical distance measurements. The on-line data has been acquired with the help of a digitized tablet PC. Also we verify our algorithm in SVC 2004 database. In the proposed signature-verification algorithm, an input signal of the signature undergoes preprocessing procedures including smoothing, rotation, shifting and size normalization.