4 research outputs found
On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator
Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise
Secure fingerprinting on sound foundations
The rapid development and the advancement of digital technologies open a variety of opportunities to consumers and content providers for using and trading digital goods. In this context, particularly the Internet has gained a major ground as a worldwiede platform for exchanging and distributing digital goods. Beside all its possibilities and advantages digital technology can be misuesd to breach copyright regulations: unauthorized use and illegal distribution of intellectual property cause authors and content providers considerable loss. Protections of intellectual property has therefore become one of the major challenges of our information society. Fingerprinting is a key technology in copyright protection of intellectual property. Its goal is to deter people from copyright violation by allowing to provably identify the source of illegally copied and redistributed content. As one of its focuses, this thesis considers the design and construction of various fingerprinting schemes and presents the first explicit, secure and reasonably efficient construction for a fingerprinting scheme which fulfills advanced security requirements such as collusion-tolerance, asymmetry, anonymity and direct non-repudiation. Crucial for the security of such s is a careful study of the underlying cryptographic assumptions. In case of the fingerprinting scheme presented here, these are mainly assumptions related to discrete logarithms. The study and analysis of these assumptions is a further focus of this thesis. Based on the first thorough classification of assumptions related to discrete logarithms, this thesis gives novel insights into the relations between these assumptions. In particular, depending on the underlying probability space we present new reuslts on the reducibility between some of these assumptions as well as on their reduction efficency.Die Fortschritte im Bereich der Digitaltechnologien bieten Konsumenten,
Urhebern und Anbietern große Potentiale für innovative Geschäftsmodelle
zum Handel mit digitalen Gütern und zu deren Nutzung. Das Internet stellt
hierbei eine interessante Möglichkeit zum Austausch und zur Verbreitung
digitaler Güter dar. Neben vielen Vorteilen kann die Digitaltechnik jedoch
auch missbräuchlich eingesetzt werden, wie beispielsweise zur Verletzung
von Urheberrechten durch illegale Nutzung und Verbreitung von Inhalten,
wodurch involvierten Parteien erhebliche Schäden entstehen können. Der
Schutz des geistigen Eigentums hat sich deshalb zu einer der besonderen
Herausforderungen unseres Digitalzeitalters entwickelt.
Fingerprinting ist eine Schlüsseltechnologie zum Urheberschutz. Sie hat
das Ziel, vor illegaler Vervielfältigung und Verteilung digitaler Werke abzuschrecken, indem sie die Identifikation eines Betrügers und das Nachweisen
seines Fehlverhaltens ermöglicht. Diese Dissertation liefert als eines ihrer Ergebnisse die erste explizite, sichere und effiziente Konstruktion, welche die
Berücksichtigung besonders fortgeschrittener Sicherheitseigenschaften wie
Kollusionstoleranz, Asymmetrie, Anonymität und direkte Unabstreitbarkeit
erlaubt.
Entscheidend für die Sicherheit kryptographischer Systeme ist die präzise
Analyse der ihnen zugrunde liegenden kryptographischen Annahmen. Den
im Rahmen dieser Dissertation konstruierten Fingerprintingsystemen liegen
hauptsächlich kryptographische Annahmen zugrunde, welche auf diskreten
Logarithmen basieren. Die Untersuchung dieser Annahmen stellt einen weiteren
Schwerpunkt dieser Dissertation dar. Basierend auf einer hier erstmals
in der Literatur vorgenommenen Klassifikation dieser Annahmen werden
neue und weitreichende Kenntnisse über deren Zusammenhänge gewonnen.
Insbesondere werden, in Abhängigkeit von dem zugrunde liegenden Wahrscheinlichkeitsraum, neue Resultate hinsichtlich der Reduzierbarkeit dieser
Annahmen und ihrer Reduktionseffizienz erzielt
Anti-Collusion Fingerprinting for Multimedia
Digital fingerprinting is a technique for identifyingusers who might try to use multimedia content for unintendedpurposes, such as redistribution. These fingerprints are typicallyembedded into the content using watermarking techniques that aredesigned to be robust to a variety of attacks. A cost-effectiveattack against such digital fingerprints is collusion, whereseveral differently marked copies of the same content are combinedto disrupt the underlying fingerprints. In this paper, weinvestigate the problem of designing fingerprints that canwithstand collusion and allow for the identification of colluders.We begin by introducing the collusion problem for additiveembedding. We then study the effect that averaging collusion hasupon orthogonal modulation. We introduce an efficient detectionalgorithm for identifying the fingerprints associated with Kcolluders that requires O(K log(n/K)) correlations for agroup of n users. We next develop a fingerprinting scheme basedupon code modulation that does not require as many basis signalsas orthogonal modulation. We propose a new class of codes, calledanti-collusion codes (ACC), which have the property that thecomposition of any subset of K or fewer codevectors is unique.Using this property, we can therefore identify groups of K orfewer colluders. We present a construction of binary-valued ACCunder the logical AND operation that uses the theory ofcombinatorial designs and is suitable for both the on-off keyingand antipodal form of binary code modulation. In order toaccommodate n users, our code construction requires onlyO(sqrt{n}) orthogonal signals for a given number of colluders.We introduce four different detection strategies that can be usedwith our ACC for identifying a suspect set of colluders. Wedemonstrate the performance of our ACC for fingerprintingmultimedia and identifying colluders through experiments usingGaussian signals and real images.This paper has been submitted to IEEE Transactions on Signal Processing</I