fadec - Free download as PDF File (.pdf), Text File (.txt) or read online for free. 1. Full Authority Digital Engine Control (FADEC) systems consist of a digital computer called an Electronic Engine Controller (EEC) that monitors and …
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→ WhatsApp: +86 18221755073DOI: 10.1109/IJCNN48605.2020.9207635 Corpus ID: 221544050; FaDec: A Fast Decision-based Attack for Adversarial Machine Learning
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→ WhatsApp: +86 18221755073Introduced in 1989, our FADEC 2 models were built for both narrow and wide-body aircraft manufactured by Boeing and Airbus. Engine Models. CF6-80E; CFM56-7B; CFM56-5C; Platforms. Airbus: A330, A340; Boeing: 737; Learn …
→ WhatsApp: +86 18221755073Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their ML modules. Traditionally, the black-box attacks are either transfer attacks requiring model stealing, or score/decision-based gradient estimation attacks requiring a large number of queries. In …
→ WhatsApp: +86 18221755073that the proposed FaDec attack is 16x faster compared to the state-of-the-art decision-based attacks, and generates an attack image with better imperceptibility for a much lesser number of
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→ WhatsApp: +86 18221755073For instance, in case III, the perturbation norm (d) of the attack noise increases, 1.54 to 293.1. - "FaDec: A Fast Decision-based Attack for Adversarial Machine Learning" Fig. 1: Adversarial Examples generated by the decision-based attack [31] when the number of queries is restricted to 1000 and 10000. This analysis shows that the perception ...
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→ WhatsApp: +86 18221755073Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their ML modules. Traditionally, the black-box attacks are either transfer ... FaDec: A Fast Decision-based Attack for Adversarial Machine Learning. ABDULLAH HANIF.
→ WhatsApp: +86 18221755073Fig. 7: Effects of δmin, n and theta on the convergence of FaDec attack (perturbation norms vs. the number of queries) for state-of-the-art CNNs trained on the GTSRB dataset. The dotted vertical lines in the figure show the number of queries required to converge the state-of-the-art decision-based attack [31]. These analyses show that in all the cases, our FaDec attack using …
→ WhatsApp: +86 18221755073Contribute to hashi0203/fadec development by creating an account on GitHub. This is a novel FPGA-based accelerator for a depth estimation method "DeepVideoMVS", which alternates between traditional image/video processing algorithms and DNNs.We employ HW/SW co-design to appropriately utilize heterogeneous components in modern SoC FPGAs, such as …
→ WhatsApp: +86 18221755073Fig. 10: Comparison of FaDec for different cases with the state-of-the-art decision-based attack [31] for different values of δmin, n and θ. The maximum number of allowed queries Qmax is fixed to 1000. The analyses show that FaDec generates better imperceptibility of adversarial noise as compared to state-of-the-art decision-based attack [31]. For example, in the case the decision …
→ WhatsApp: +86 18221755073The experimental analysis shows that the proposed FaDec attack is 16x faster compared to the state-of-the-art decision-based attacks, and generates an attack image with better imperceptibility for a much lesser number of iterations, thereby making the attack more powerful in practical scenarios. Due to the excessive use of cloud-based machine learning (ML) services, the smart …
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→ WhatsApp: +86 18221755073Fig. 8: Effects of δmin, n and theta on the convergence of FaDec attack (perturbation norms vs. the number of queries) for state-of-the-art CNNs trained on the R-10 dataset. The dotted vertical lines in the figure show the number of queries required to converge the state-of-the-art decision-based attack [31]. These analyses show that in all the cases, our FaDec attack using …
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