Gotowe bibliografie tematyczne / Fast Gradient Sign Method

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Fast Gradient Sign Method”

Autor: Grafiati
Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Fast Gradient Sign Method"

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Zou, Junhua, Yexin Duan, Boyu Li, Wu Zhang, Yu Pan, and Zhisong Pan. "Making Adversarial Examples More Transferable and Indistinguishable." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3662–70. http://dx.doi.org/10.1609/aaai.v36i3.20279.

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Fast gradient sign attack series are popular methods that are used to generate adversarial examples. However, most of the approaches based on fast gradient sign attack series cannot balance the indistinguishability and transferability due to the limitations of the basic sign structure. To address this problem, we propose a method, called Adam Iterative Fast Gradient Tanh Method (AI-FGTM), to generate indistinguishable adversarial examples with high transferability. Besides, smaller kernels and dynamic step size are also applied to generate adversarial examples for further increasing the attack
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Wibawa, Sigit. "Analysis of Adversarial Attacks on AI-based With Fast Gradient Sign Method." International Journal of Engineering Continuity 2, no. 2 (2023): 72–79. http://dx.doi.org/10.58291/ijec.v2i2.120.

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Artificial intelligence (AI) has become a key driving force in sectors from transportation to healthcare, and is opening up tremendous opportunities for technological advancement. However, behind this promising potential, AI also presents serious security challenges. This article aims to investigate attacks on AI and security challenges that must be faced in the era of artificial intelligence, this research aims to simulate and test the security of AI systems due to adversarial attacks. We can use the Python programming language for this, using several libraries and tools. One that is very pop
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Sun, Guangling, Yuying Su, Chuan Qin, Wenbo Xu, Xiaofeng Lu, and Andrzej Ceglowski. "Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples." Mathematical Problems in Engineering 2020 (May 11, 2020): 1–17. http://dx.doi.org/10.1155/2020/8319249.

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Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigations have increasingly shown DNNs to be highly vulnerable when adversarial examples are used as input. Here, we present a comprehensive defense framework to protect DNNs against adversarial examples. First, we present statistical and minor alteration detectors to filter out adversarial examples contaminated by noticeable and unnoticeable perturbations, respectively. Then, we ensemble the detectors, a deep Residual Generative Network (ResGN), and an adversarially trained targeted network, to cons
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Kim, Hoki, Woojin Lee, and Jaewook Lee. "Understanding Catastrophic Overfitting in Single-step Adversarial Training." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8119–27. http://dx.doi.org/10.1609/aaai.v35i9.16989.

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Although fast adversarial training has demonstrated both robustness and efficiency, the problem of "catastrophic overfitting" has been observed. This is a phenomenon in which, during single-step adversarial training, the robust accuracy against projected gradient descent (PGD) suddenly decreases to 0% after a few epochs, whereas the robust accuracy against fast gradient sign method (FGSM) increases to 100%. In this paper, we demonstrate that catastrophic overfitting is very closely related to the characteristic of single-step adversarial training which uses only adversarial examples with the m
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Saxena, Rishabh, Amit Sanjay Adate, and Don Sasikumar. "A Comparative Study on Adversarial Noise Generation for Single Image Classification." International Journal of Intelligent Information Technologies 16, no. 1 (2020): 75–87. http://dx.doi.org/10.4018/ijiit.2020010105.

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With the rise of neural network-based classifiers, it is evident that these algorithms are here to stay. Even though various algorithms have been developed, these classifiers still remain vulnerable to misclassification attacks. This article outlines a new noise layer attack based on adversarial learning and compares the proposed method to other such attacking methodologies like Fast Gradient Sign Method, Jacobian-Based Saliency Map Algorithm and DeepFool. This work deals with comparing these algorithms for the use case of single image classification and provides a detailed analysis of how eac
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Yang, Bo, Kaiyong Xu, Hengjun Wang, and Hengwei Zhang. "Random Transformation of image brightness for adversarial attack." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 1693–704. http://dx.doi.org/10.3233/jifs-211157.

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Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding small, human-imperceptible perturbations to the original images, but make the model output inaccurate predictions. Before DNNs are deployed, adversarial attacks can thus be an important method to evaluate and select robust models in safety-critical applications. However, under the challenging black-box setting, the attack success rate, i.e., the transferability of adversarial examples, still needs to be improved. Based on image augmentation methods, this paper found that random transformation of ima
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Trinh Quang Kien. "Improving the robustness of binarized neural network using the EFAT method." Journal of Military Science and Technology, CSCE5 (December 15, 2021): 14–23. http://dx.doi.org/10.54939/1859-1043.j.mst.csce5.2021.14-23.

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In recent years with the explosion of research in artificial intelligence, deep learning models based on convolutional neural networks (CNNs) are one of the promising architectures for practical applications thanks to their reasonably good achievable accuracy. However, CNNs characterized by convolutional layers often have a large number of parameters and computational workload, leading to large energy consumption for training and network inference. The binarized neural network (BNN) model has been recently proposed to overcome that drawback. The BNNs use binary representation for the inputs an
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Hirano, Hokuto, and Kazuhiro Takemoto. "Simple Iterative Method for Generating Targeted Universal Adversarial Perturbations." Algorithms 13, no. 11 (2020): 268. http://dx.doi.org/10.3390/a13110268.

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Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, a single perturbation known as the universal adversarial perturbation (UAP) can foil most classification tasks conducted by DNNs. Thus, different methods for generating UAPs are required to fully evaluate the vulnerability of DNNs. A realistic evaluation would be with cases that consider targeted attacks; wherein the generated UAP causes the DNN to classify an input into a specific class. However, the development of UAPs for targeted attacks has largely fallen behind that of UAPs for non-targeted attacks. Therefo
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An, Tong, Tao Zhang, Yanzhang Geng, and Haiquan Jiao. "Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter." Scientific Programming 2021 (August 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/8826868.

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The proportionate affine projection sign subband adaptive filter (PAP-SSAF) has a better performance than the affine projection sign subband adaptive filter (AP-SSAF) when we eliminate the echoes. Still, the robustness of the PAP-SSAF algorithm is insufficient under unknown environmental conditions. Besides, the best balance remains to be found between low steady-state misalignment and fast convergence rate. In order to solve this problem, we propose a normalized combination of PAP-SSAF (NCPAP-SSAF) based on the normalized adaption schema. In this paper, a power normalization adaptive rule for
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Kadhim, Ansam, and Salah Al-Darraji. "Face Recognition System Against Adversarial Attack Using Convolutional Neural Network." Iraqi Journal for Electrical and Electronic Engineering 18, no. 1 (2021): 1–8. http://dx.doi.org/10.37917/ijeee.18.1.1.

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Face recognition is the technology that verifies or recognizes faces from images, videos, or real-time streams. It can be used in security or employee attendance systems. Face recognition systems may encounter some attacks that reduce their ability to recognize faces properly. So, many noisy images mixed with original ones lead to confusion in the results. Various attacks that exploit this weakness affect the face recognition systems such as Fast Gradient Sign Method (FGSM), Deep Fool, and Projected Gradient Descent (PGD). This paper proposes a method to protect the face recognition system aga
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Rozprawy doktorskie na temat "Fast Gradient Sign Method"

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Zhang, Zichen. "Local gradient estimate for porous medium and fast diffusion equations by Martingale method." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:551f79f8-b309-4a1f-8afa-c7dc433dad82.

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This thesis focuses on a certain type of nonlinear parabolic partial differential equations, i.e. PME and FDE. Chapter 1 consists of a survey on results related to PME and FDE, and a short review on some works about deriving gradient estimates in probabilistic ways. In Chapter 2 we estimate gradient on space variables of solutions to the heat equation on Euclidean space. The main idea is to construct two semimartingales by letting the solution and its gradient running backward on the path space of a diffusion process. Estimates derived from decompositions of those two semimartingales are then
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Pester, M., and S. Rjasanow. "A parallel version of the preconditioned conjugate gradient method for boundary element equations." Universitätsbibliothek Chemnitz, 1998. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-199800455.

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The parallel version of precondition techniques is developed for matrices arising from the Galerkin boundary element method for two-dimensional domains with Dirichlet boundary conditions. Results were obtained for implementations on a transputer network as well as on an nCUBE-2 parallel computer showing that iterative solution methods are very well suited for a MIMD computer. A comparison of numerical results for iterative and direct solution methods is presented and underlines the superiority of iterative methods for large systems.
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Strauss, Arne Karsten. "Numerical Analysis of Jump-Diffusion Models for Option Pricing." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/33917.

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Jump-diffusion models can under certain assumptions be expressed as partial integro-differential equations (PIDE). Such a PIDE typically involves a convection term and a nonlocal integral like for the here considered models of Merton and Kou. We transform the PIDE to eliminate the convection term, discretize it implicitly using finite differences and the second order backward difference formula (BDF2) on a uniform grid. The arising dense linear system is solved by an iterative method, either a splitting technique or a circulant preconditioned conjugate gradient method. Exploiting the Fast Four
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Alli-Oke, Razak Olusegun. "Robustness and optimization in anti-windup control." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/robustness-and-optimization-in-antiwindup-control(8b98c920-90c3-4fbc-95a8-0cc7ae2a607a).html.

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This thesis is broadly concerned with online-optimizing anti-windup control. These are control structures that implement some online-optimization routines to compensate for the windup effects in constrained control systems. The first part of this thesis examines a general framework for analyzing robust preservation in anti-windup control systems. This framework - the robust Kalman conjecture - is defined for the robust Lur’e problem. This part of the thesis verifies this conjecture for first-order plants perturbed by various norm-bounded unstructured uncertainties. Integral quadratic constrain
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Vivek, B. S. "Towards Learning Adversarially Robust Deep Learning Models." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4488.

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Deep learning models have shown impressive performance across a wide spectrum of computer vision applications, including medical diagnosis and autonomous driving. One of the major concerns that these models face is their susceptibility to adversarial samples: samples with small, crafted noise designed to manipulate the model’s prediction. A defense mechanism named Adversarial Training (AT) shows promising results against these attacks. This training regime augments mini-batches with adversaries. However, to scale this training to large networks and datasets, fast and simple methods (e.g.
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Juan, Yu-Chin, and 阮毓欽. "A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/32077403329819649481.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>102<br>Matrix factorization is known to be an effective method for recommender systems that are given only the ratings from users to items. Currently, stochastic gradient (SG) method is one of the most popular algorithms for matrix factorization. However, as a sequential approach, SG is difficult to be parallelized for handling web-scale problems. In this thesis, we develop a fast parallel SG method, FPSG, for shared memory systems. By dramatically reducing the cache-miss rate and carefully addressing the load balance of threads, FPSG is more efficient than state-of
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WANG, CHIH-HAO, and 王志豪. "Solving Scattering Problems of Large-Sized Conducting Objects by Conjugate Gradient Algorithm with Fast Multipole Method." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/39689963107809382071.

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碩士<br>國立海洋大學<br>電機工程學系<br>89<br>In this thesis, we use the method of moment (MoM) to solve the electromagnetic scattering problems. A three-dimension arbitrary-shaped conductive object is divided into triangular patches, and the integral equation is discretized by MoM. Then a conjugate gradient method (CGM) is used to iteratively solve the resulting matrix equation for unknown expansion coefficients for the surface current. But when the number of unknowns is large, the CGM takes more time at each iteration. In view of this, we use the fast multipole method (FMM) to speed up the matrix-vector
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Yu, Zhiru. "A CG-FFT Based Fast Full Wave Imaging Method and its Potential Industrial Applications." Diss., 2015. http://hdl.handle.net/10161/11344.

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<p>This dissertation focuses on a FFT based forward EM solver and its application in inverse problems. The main contributions of this work are two folded. On the one hand, it presents the first scaled lab experiment system in the oil and gas industry for through casing hydraulic fracture evaluation. This system is established to validate the feasibility of contrasts enhanced fractures evaluation. On the other hand, this work proposes a FFT based VIE solver for hydraulic fracture evaluation. This efficient solver is needed for numerical analysis of such problem. The solver is then generalized t
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Książki na temat "Fast Gradient Sign Method"

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Pan, Victor. A fast, preconditioned conjugate gradient Toeplitz solver. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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Evtushenko, Yury, Vladimir Zubov, and Anna Albu. Optimal control of thermal processes with phase transitions. LCC MAKS Press, 2021. http://dx.doi.org/10.29003/m2449.978-5-317-06677-2.

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The optimal control of the metal solidification process in casting is considered. Quality of the obtained detail greatly depends on how the crystallization process proceeds. It is known that to obtain a model of a good quality it is desirable that the phase interface would be as close as possible to a plane and that the speed of its motion would be close to prescribed. The proposed mathematical model of the crystallization process is based on a three dimensional two phase initial-boundary value problem of the Stefan type. The velocity of the mold in the furnace is used as the control. The cont
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Części książek na temat "Fast Gradient Sign Method"

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Muncsan, Tamás, and Attila Kiss. "Transferability of Fast Gradient Sign Method." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55187-2_3.

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Xia, Xiaoyan, Wei Xue, Pengcheng Wan, Hui Zhang, Xinyu Wang, and Zhiting Zhang. "FCGSM: Fast Conjugate Gradient Sign Method for Adversarial Attack on Image Classification." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2287-1_98.

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Wang, Jiangqin, and Wen Gao. "A Fast Sign Word Recognition Method for Chinese Sign Language." In Advances in Multimodal Interfaces — ICMI 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40063-x_78.

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Tian, Zhiyi, Chenhan Zhang, Lei Cui, and Shui Yu. "GSMI: A Gradient Sign Optimization Based Model Inversion Method." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3_6.

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Chen, Cheng, Zhiguang Wang, Yongnian Fan, Xue Zhang, Dawei Li, and Qiang Lu. "Nesterov Adam Iterative Fast Gradient Method for Adversarial Attacks." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15919-0_49.

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Necoara, I. "Rate Analysis of Inexact Dual Fast Gradient Method for Distributed MPC." In Intelligent Systems, Control and Automation: Science and Engineering. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7006-5_10.

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Chen, Li, Hongzhi Zhang, Dongwei Ren, David Zhang, and Wangmeng Zuo. "Fast Augmented Lagrangian Method for Image Smoothing with Hyper-Laplacian Gradient Prior." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45643-9_2.

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Chernov, Alexey, Pavel Dvurechensky, and Alexander Gasnikov. "Fast Primal-Dual Gradient Method for Strongly Convex Minimization Problems with Linear Constraints." In Discrete Optimization and Operations Research. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44914-2_31.

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Cátedra, M. F., Rafael P. Torres, and Jesús G. Cuevas. "A method to analyze scattering from general periodic screens using Fast Fourier Transform and Conjugate Gradient method." In Electromagnetic Modelling and Measurements for Analysis and Synthesis Problems. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3232-9_9.

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Lin, Yuhui, Zhiyi Qu, Yu Zhang, and Huiyi Han. "A Fast and Accurate Pupil Localization Method Using Gray Gradient Differential and Curve Fitting." In Proceedings of the 4th International Conference on Computer Engineering and Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11104-9_58.

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Streszczenia konferencji na temat "Fast Gradient Sign Method"

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Liu, Yujie, Shuai Mao, Xiang Mei, Tao Yang, and Xuran Zhao. "Sensitivity of Adversarial Perturbation in Fast Gradient Sign Method." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002856.

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Xu, Jin. "Generate Adversarial Examples by Nesterov-momentum Iterative Fast Gradient Sign Method." In 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2020. http://dx.doi.org/10.1109/icsess49938.2020.9237700.

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Hong, In-pyo, Gyu-ho Choi, Pan-koo Kim, and Chang Choi. "Security Verification Software Platform of Data-efficient Image Transformer Based on Fast Gradient Sign Method." In SAC '23: 38th ACM/SIGAPP Symposium on Applied Computing. ACM, 2023. http://dx.doi.org/10.1145/3555776.3577731.

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Hassan, Muhammad, Shahzad Younis, Ahmed Rasheed, and Muhammad Bilal. "Integrating single-shot Fast Gradient Sign Method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers." In Fourteenth International Conference on Machine Vision (ICMV 2021), edited by Wolfgang Osten, Dmitry Nikolaev, and Jianhong Zhou. SPIE, 2022. http://dx.doi.org/10.1117/12.2623585.

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Reyes-Amezcua, Ivan, Gilberto Ochoa-Ruiz, and Andres Mendez-Vazquez. "Transfer Robustness to Downstream Tasks Through Sampling Adversarial Perturbations." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2023. Journal of LatinX in AI Research, 2023. http://dx.doi.org/10.52591/lxai2023061811.

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Due to the vulnerability of deep neural networks to adversarial attacks, adversarial robustness has grown to be a crucial problem in deep learning. Recent research has demonstrated that even small perturbations to the input data can have a large impact on the model’s output, exposing them susceptible to malicious attacks. In this work, we propose Delta Data Augmentation (DDA), a data augmentation method for enhancing transfer robustness by sampling extracted perturbations from trained models against adversarial attacks. The main idea of our work is to generate adversarial perturbations and to
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Silva, Gabriel H. N. Espindola da, Rodrigo Sanches Miani, and Bruno Bogaz Zarpelão. "Investigando o Impacto de Amostras Adversárias na Detecção de Intrusões em um Sistema Ciberfísico." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbrc.2023.488.

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Neste artigo, investigamos o impacto que amostras adversárias causam em algoritmos de aprendizado de máquina supervisionado utilizados para detectar ataques em um sistema ciberfísico. O estudo leva em consideração o cenário onde um atacante consegue obter acesso a dados do sistema alvo que podem ser utilizados para o treinamento do modelo adversário. O objetivo do atacante é gerar amostras maliciosas utilizando aprendizado de máquina adversário para enganar os modelos implementados para detecção de intrusão. Foi observado através dos ataques FGSM (Fast Gradient Sign Method) e JSMA (Jacobian Sa
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Mohandas, Sreenivasan, Naresh Manwani, and Durga Dhulipudi. "Momentum Iterative Gradient Sign Method Outperforms PGD Attacks." In 14th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010938400003116.

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Chen, Annie I., and Asuman Ozdaglar. "A fast distributed proximal-gradient method." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483273.

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Mineo, Taiyo, and Hayaru Shouno. "Improving Convergence Rate of Sign Algorithm using Natural Gradient Method." In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616060.

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Sujee, R., and S. Padmavathi. "Fast Texture Classification using Gradient Histogram Method." In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2020. http://dx.doi.org/10.1109/icaccs48705.2020.9074355.

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Raporty organizacyjne na temat "Fast Gradient Sign Method"

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Peter W. Carr, K.M. Fuller, D.R. Stoll, L.D. Steinkraus, M.S. Pasha, and Glenn G. Hardin. Fast Gradient Elution Reversed-Phase HPLC with Diode-Array Detection as a High Throughput Screening Method for Drugs of Abuse. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/892807.

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