Digitale Vertrauenskulturen

Wie sich die Transformation moderner Gesellschaften in den nächsten Jahren fortsetzt, hängt ganz zentral von der Entwicklung, Implementierung und sozialen Kontrolle der GNR-Technologien (der Kombination aus Gen-, Nano- und Robotertechnologie) ab. Die Diskussion zur künstlichen Intelligenz, die im letzten Jahrzehnt geführt worden ist, hat mit dem Gebiet der Robotertechnologie gleichsam eine neue Arena gefunden und sich auf dieses Gebiet verlagert. Hier werden jetzt grundlegende, auch pädagogisch zentrale Fragen, wie z.B. die nach einem Personenkonzept, diskutiert (vgl. Richards u.a. 2002). Zentrale Bedenken, die sich auf die mit den neuen Technologien verbundenen Gefahren stützen, sind immer wieder vorgetragen worden (Joy 2000; Moravec 1999). Ohne diese verzweigte Debatte an dieser Stelle rekonstruieren zu wollen, kann doch ein Befund in verallgemeinernder Absicht hervorgehoben werden: In dem Maße, in dem Gesellschaften aufgrund des Einsatzes neuer Technologien einen Komplexitätsschub aufweisen, der sich bis in die Lebenswelten einzelner Menschen hinein auswirkt, rückt ein „Mechanismus“ von Sozialität immer stärker in das Zentrum der Aufmerksamkeit: Vertrauen. Nicht nur aus der hier herangezogenen Perspektive wird diese Ressource prekär. Vielmehr ist seit Beginn der neunzehnhundertneunziger Jahre ein Ansteigen der Publikationen zu dem Thema Vertrauen aus verschiedenen Perspektiven zu konstatieren, und zwar in Soziologie, Pädagogik, Philosophie, Politikwissenschaft und Ökonomie. Vertrauen wird als elementare Voraussetzung sozialer Prozesse gesehen. Wenn Vertrauen aber nicht mehr als selbstverständliche Voraussetzung sozialer Prozesse verstanden werden kann, häufen sich Maßnahmen zur Vertrauensbildung, gerät das Phänomen Vertrauen also in den Fokus der systematischen Reflexion

Defending Informational Sovereignty by Detecting Deepfakes: Risks and Opportunities of an AI-Based Detector for Deepfake-Based Disinformation and Illegal Activities

This paper will first investigate possible contributions that an AI-based detector for deepfakes could make to the challenge of responding to disinformation as a threat to democracy. Second, this paper will also investigate the implications of such a tool - which was developed, among other reasons, for security purposes - for the emerging European discourse on digital sovereignty in a global environment. While disinformation is surely not a new topic, recent technological developments relating to AI-generated deepfakes have increased the manipulative potential of video and audio-based contents spread online, making it a specific but important current challenge in the global and interconnected information context

MULTIMEDIA AND SECURITY ECRYPT: EUROPEAN NETWORK OF EXCELLENCE IN CRYPTOLOGY

Die Abkürzung ECRYPT /1/ steht für „European Network of Excellence in Cryptology”. Das Netzwerk ist ein Zusammenschluss von ca. 180 europäischen Forschern und Entwicklern, an dem auch Informatiker der Otto-von-Guericke-Universität Magdeburg beteiligt sind. Das Projekt, in dessen Mittelpunkt die Forschungen zur Sicherung von Multimediadaten (Digital Rights Management) stehen, wird von der Europäischen Union über einen Zeitraum von vier Jahren gefördert. Die Forscher wollen Methoden entwickeln, um Musik, Bilder oder Videos mit zusätzlichen Schutzmechanismen zu versehen, mit dem Ziel Manipulationen zu erkennen oder bspw. Raubkopien zu verhindern oder aufzuspüren. Das Advanced Multimedia and Security Lab (AMSL) der Arbeitsgruppe Multimedia and Security an der Otto-von-Guericke-Universität leitet zusammen mit Stefan Katzenbeisser (TU München) seit Anfang 2004 das „Watermarking Virtual Lab“ (WAVILA), einen Teilbereich des Netzwerkes ECRYPT. In diesem Verbund arbeiten verschiedene Arbeitsgruppen, zum Beispiel aus Italien, Spanien, Frankreich, Deutschland, Belgien, der Schweiz und den Niederlanden, zusammen. Der Fokus der gemeinsamen Arbeit liegt dabei im Bereich der digitalen Wasserzeichen. Ihre Eigenschaften und Einsatzgebiete werden analysiert und es werden theoretische Grundlagen für Sicherheitsmodelle und für die Definitionen digitaler Wasserzeichen erarbeitet. In diesem Beitrag sollen die Motivation und die neuen Herausforderungen von ECRYPT, sowie die Ziele des Netzwerkes vorgestellt werden. Des Weiteren werden Arbeiten aus dem Bereich Wasserzeichen Benchmarking sowie Algorithmenentwurf präsentiert, um einen Einblick in das Umfeld zu ermöglichen

Model-based data generation for the evaluation of functional reliability and resilience of distributed machine learning systems against abnormal cases

Future production technologies will comprise a multitude of systems whose core functionality is closely related to machine-learned models. Such systems require reliable components to ensure the safety of workers and their trust in the systems. The evaluation of the functional reliability and resilience of systems based on machine-learned models is generally challenging. For this purpose, appropriate test data must be available, which also includes abnormal cases. These abnormal cases can be unexpected usage scenarios, erroneous inputs, accidents during operation or even the failure of certain subcomponents. In this work, approaches to the model-based generation of an arbitrary abundance of data representing such abnormal cases are explored. Such computer-based generation requires domain-specific approaches, especially with respect to the nature and distribution of the data, protocols used, or domain-specific communication structures. In previous work, we found that different use cases impose different requirements on synthetic data, and the requirements in turn imply different generation methods [1]. Based on this, various use cases are identified and different methods for computer-based generation of realistic data, as well as for the quality assessment of such data, are explored. Ultimately we explore the use of Federated Learning (FL) to address data privacy and security challenges in Industrial Control Systems. FL enables local model training while keeping sensitive information decentralized and private to their owners. In detail, we investigate whether FL can benefit clients with limited knowledge by leveraging collaboratively trained models that aggregate client-specific knowledge distributions. We found that in such scenarios federated training results in a significant increase in classification accuracy by 31.3% compared to isolated local training. Furthermore, as we introduce Differential Privacy, the resulting model achieves on par accuracy of 99.62% to an idealized case where data is independent and identically distributed across clients

A Context Model for Microphone Forensics and its Application in Evaluations

ABSTRACT In this paper we first design a suitable context model for microphone recordings, formalising and describing the involved signal processing pipeline and the corresponding influence factors. As a second contribution we apply the context model to devise empirical investigations about: a) the identification of suitable classification algorithms for statistical pattern recognition based microphone forensics, evaluating 74 supervised classification techniques and 8 clusterers; b) the determination of suitable features for the pattern recognition (with very good results for second order derivative MFCC based features), showing that a reduction to the 20 best features has no negative influence to the classification accuracy, but increases the processing speed by factor 30; c) the determination of the influence of changes in the microphone orientation and mounting on the classification performance, showing that the first has no detectable influence, while the latter shows a strong impact under certain circumstances; d) the performance achieved in using the statistical pattern recognition based microphone forensics approach for the detection of audio signal compositions. MOTIVATION AND INTRODUCTION The past years have seen significant advances in digital image forensics. An overview of currently established authentication approaches for this domain is given by Hany Farid 5 . In contrast to image forensics, in the field of audio forensics so far only a limited number of approaches can be found, even though audio forensics can be considered to be very interesting for application scenarios where trust in authenticity and integrity of audio signals might be required, e.g. for evidences in court cases or in the ingest phase of secure digital long term archives. The currently existing approaches for microphone forensics (MF; a.k.a. recording forensics or recording source forensics) -as one of the most important sub-categories in audio forensics, can be classified into three classes: ENF-based approaches: One quite mature, but physically complex approach found in literature (e.g. Grigoras 7 ) is the usage of the electric network frequency (ENF) in recordings to evaluate digital audio authenticity. The complex electrophysical requirements for this approach are summarized by Grigoras et al. Time domain and local phenomena based evaluations: In 2010 Malik and Farid 2 describe a technique to model and estimate the amount of reverberation in an audio recording. Because reverberation depends on the shape and composition of a room, differences in the estimated reverberation can be used in a forensic setting for authentication. The usage of similar characteristics can be found in closely related research fields like e.g. in the works from Maher 9 on gunshot characterization. Yang et al. In this paper we extend the current state-of-the-art by investigations work described by Oermann et al. 14 and Kraetzer et al. 1 . As a first important step we design a suitable context model for microphone recordings, formalising and describing the involved 5-stage recording process pipeline. Second, we apply the context model to devise empirical investigations aiming at the generation of required domain knowledge. These questions about the provenance, persistence and uniqueness of a sensor patterns in microphones are raised by previous work in this fiel

Observation of the Rare Decay of the η Meson to Four Muons

A search for the rare η→μ+μ−μ+μ− double-Dalitz decay is performed using a sample of proton-proton collisions, collected by the CMS experiment at the CERN LHC with high-rate muon triggers during 2017 and 2018 and corresponding to an integrated luminosity of 101  fb−1. A signal having a statistical significance well in excess of 5 standard deviations is observed. Using the η→μ+μ− decay as normalization, the branching fraction B(η→μ+μ−μ+μ−)=[5.0±0.8(stat)±0.7(syst)±0.7(B2μ)]×10−9 is measured, where the last term is the uncertainty in the normalization channel branching fraction. This work achieves an improved precision of over 5 orders of magnitude compared to previous results, leading to the first measurement of this branching fraction, which is found to agree with theoretical predictions

Search for new physics in multijet events with at least one photon and large missing transverse momentum in proton-proton collisions at 13 TeV

A search for new physics in final states consisting of at least one photon, multiple jets, and large missing transverse momentum is presented, using proton-proton collision events at a center-of-mass energy of 13 TeV. The data correspond to an integrated luminosity of 137 fb−1, recorded by the CMS experiment at the CERN LHC from 2016 to 2018. The events are divided into mutually exclusive bins characterized by the missing transverse momentum, the number of jets, the number of b-tagged jets, and jets consistent with the presence of hadronically decaying W, Z, or Higgs bosons. The observed data are found to be consistent with the prediction from standard model processes. The results are interpreted in the context of simplified models of pair production of supersymmetric particles via strong and electroweak interactions. Depending on the details of the signal models, gluinos and squarks of masses up to 2.35 and 1.43 TeV, respectively, and electroweakinos of masses up to 1.23 TeV are excluded at 95% confidence level

Measurements of inclusive and differential cross sections for the Higgs boson production and decay to four-leptons in proton-proton collisions at s \sqrt{s} = 13 TeV

Measurements of the inclusive and differential fiducial cross sections for the Higgs boson production in the H → ZZ → 4ℓ (ℓ = e, μ) decay channel are presented. The results are obtained from the analysis of proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb−1. The measured inclusive fiducial cross section is 2.73 ± 0.26 fb, in agreement with the standard model expectation of 2.86 ± 0.1 fb. Differential cross sections are measured as a function of several kinematic observables sensitive to the Higgs boson production and decay to four leptons. A set of double-differential measurements is also performed, yielding a comprehensive characterization of the four leptons final state. Constraints on the Higgs boson trilinear coupling and on the bottom and charm quark coupling modifiers are derived from its transverse momentum distribution. All results are consistent with theoretical predictions from the standard model