8 research outputs found

    Dynamic Backdoors with Global Average Pooling

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    Outsourced training and machine learning as a service have resulted in novel attack vectors like backdoor attacks. Such attacks embed a secret functionality in a neural network activated when the trigger is added to its input. In most works in the literature, the trigger is static, both in terms of location and pattern. The effectiveness of various detection mechanisms depends on this property. It was recently shown that countermeasures in image classification, like Neural Cleanse and ABS, could be bypassed with dynamic triggers that are effective regardless of their pattern and location. Still, such backdoors are demanding as they require a large percentage of poisoned training data. In this work, we are the first to show that dynamic backdoor attacks could happen due to a global average pooling layer without increasing the percentage of the poisoned training data. Nevertheless, our experiments in sound classification, text sentiment analysis, and image classification show this to be very difficult in practice.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

    On the Effect of Clock Frequency on Voltage and Electromagnetic Fault Injection

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    We investigate the influence of clock frequency on the success rate of a fault injection attack. In particular, we examine the success rate of voltage and electromagnetic fault attacks for varying clock frequencies. Using three different tests that cover different components of a System-on-Chip, we perform fault injection while its CPU operates at different clock frequencies. Our results show that the attack’s success rate increases with an increase in clock frequency for both voltage and EM fault injection attacks. As the technology advances push the clock frequency further, these results can help assess the impact of fault injection attacks more accurately and develop appropriate countermeasures to address them.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

    Going in Style: Audio Backdoors Through Stylistic Transformations

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    This work explores stylistic triggers for backdoor attacks in the audio domain: dynamic transformations of malicious samples through guitar effects. We first formalize stylistic triggers – currently missing in the literature. Second, we explore how to develop stylistic triggers in the audio domain by proposing JingleBack. Our experiments confirm the effectiveness of the attack, achieving a 96% attack success rate. Our code is available in https://github.com/skoffas/going-in-style.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Cyber Securit

    Can You Hear It? Backdoor Attacks via Ultrasonic Triggers

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    This work explores backdoor attacks for automatic speech recognition systems where we inject inaudible triggers. By doing so, we make the backdoor attack challenging to detect for legitimate users and, consequently, potentially more dangerous. We conduct experiments on two versions of a speech dataset and three neural networks and explore the performance of our attack concerning the duration, position, and type of the trigger. Our results indicate that less than 1% of poisoned data is sufficient to deploy a backdoor attack and reach a 100% attack success rate. We observed that short, non-continuous triggers result in highly successful attacks. Still, since our trigger is inaudible, it can be as long as possible without raising any suspicions making the attack more effective. Finally, we conduct our attack on actual hardware and saw that an adversary could manipulate inference in an Android application by playing the inaudible trigger over the air. Cyber Securit

    More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks

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    Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due to their superior ability to learn representations for complex graph data. Due to privacy concerns and regulation restrictions, centralized GNNs can be difficult to apply to data-sensitive scenarios. Federated learning (FL) is an emerging technology developed for privacy-preserving settings when several parties need to train a shared global model collaboratively. Although several research works have applied FL to train GNNs (Federated GNNs), there is no research on their robustness to backdoor attacks.This paper bridges this gap by conducting two types of backdoor attacks in Federated GNNs: centralized backdoor attacks (CBA) and distributed backdoor attacks (DBA). Our experiments show that the DBA attack success rate is higher than CBA in almost all cases. For CBA, the attack success rate of all local triggers is similar to the global trigger, even if the training set of the adversarial party is embedded with the global trigger. To explore the properties of two backdoor attacks in Federated GNNs, we evaluate the attack performance for a different number of clients, trigger sizes, poisoning intensities, and trigger densities. Finally, we explore the robustness of DBA and CBA against two state-of-the-art defenses. We find that both attacks are robust against the investigated defenses, necessitating the need to consider backdoor attacks in Federated GNNs as a novel threat that requires custom defenses.Cyber Securit

    Search for anomaly-mediated supersymmetry breaking with the ATLAS detector based on a disappearing-track signature in pp collisions at sqrt(s) = 7TeV$

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Djobava;M. A. B. Vale;A. Valle Wemans;T. K. O. Doan;M. Dobbs;R. Dobinson;D. Dobos;E. Dobson;J. Dodd;C. Doglioni;T. Doherty;Y. Doi;J. Dolejsi;I. Dolenc;Z. Dolezal;B. A. Dolgoshein;T. Dohmae;M. Donadelli;M. Donega;J. Donini;J. Dopke;A. Doria;A. Anjos;M. Dosil;A. Dotti;M. T. Dova;J. D. Dowell;A. D. Doxiadis;A. T. Doyle;Z. Drasal;J. Drees;N. Dressnandt;H. Drevermann;C. Driouichi;M. Dris;J. Dubbert;S. Dube;E. Duchovni;G. Duckeck;A. Dudarev;F. Dudziak;M. Dührssen;I. P. Duerdoth;L. Duflot;M-A. Dufour;M. Dunford;H. Duran Yildiz;R. Duxfield;M. Dwuznik;F. Dydak;M. Düren;W. L. Ebenstein;J. Ebke;S. Eckweiler;K. Edmonds;C. A. Edwards;N. C. Edwards;W. Ehrenfeld;T. Ehrich;T. Eifert;G. Eigen;K. Einsweiler;E. Eisenhandler;T. Ekelof;M. Kacimi;M. Ellert;S. Elles;F. Ellinghaus;K. Ellis;N. Ellis;J. Elmsheuser;M. Elsing;D. Emeliyanov;R. Engelmann;A. Engl;B. Epp;A. Eppig;J. Erdmann;A. Ereditato;D. Eriksson;J. Ernst;M. Ernst;J. Ernwein;D. Errede;S. Errede;E. Ertel;M. Escalier;C. Escobar;X. Espinal Curull;B. 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Lorenz;M. Losada;P. Loscutoff;F. Lo Sterzo;M. J. Losty;X. Lou;A. Lounis;K. F. Loureiro;J. Love;P. A. Love;A. J. Lowe;F. Lu;H. J. Lubatti;C. Luci;A. Lucotte;A. Ludwig;D. Ludwig;I. Ludwig;J. Ludwig;F. Luehring;G. Luijckx;D. Lumb;L. Luminari;E. Lund;B. Lund-Jensen;B. Lundberg;J. Lundberg;J. Lundquist;M. Lungwitz;G. Lutz;D. Lynn;J. Lys;E. Lytken;H. Ma;L. L. Ma;J. A. Macana Goia;G. Maccarrone;A. Macchiolo;B. Maček;J. Machado Miguens;R. Mackeprang;R. J. Madaras;W. F. Mader;R. Maenner;T. Maeno;P. Mättig;S. Mättig;L. Magnoni;E. Magradze;Y. Mahalalel;K. Mahboubi;G. Mahout;C. Maiani;C. Maidantchik;A. Maio;S. Majewski;Y. Makida;N. Makovec;P. Mal;Pa. Malecki;P. Malecki;V. P. Maleev;F. Malek;U. Mallik;D. Malon;C. Malone;S. Maltezos;V. Malyshev;S. Malyukov;R. Mameghani;J. Mamuzic;A. Manabe;L. Mandelli;I. Mandić;R. Mandrysch;J. Maneira;P. S. Mangeard;I. D. Manjavidze;A. Mann;P. M. Manning;A. Manousakis-Katsikakis;B. Mansoulie;A. Manz;A. Mapelli;L. Mapelli;L. March;J. F. Marchand;F. Marchese;G. Marchiori;M. Marcisovsky;A. Marin;C. P. Marino;F. Marroquim;R. Marshall;Z. Marshall;F. K. Martens;S. Marti-Garcia;A. J. Martin;B. Martin;B. Martin;F. F. Martin;J. P. Martin;Ph. Martin;T. A. Martin;V. J. Martin;B. Ma

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