Handling The Adversarial Attacks
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Exploiting the Inherent Limitation of L0 Adversarial Examples
L0 AEs, a category of attacks widely considered by previous works [8,27,34,47]. To defeat attacks based on AEs, both detection and defen-sive techniques attract the research community s attention. Given an input image, the detection system outputs whether it is an AE, so that the target neural network can reject those adversarial inputs.
Uncertainty-Aware Opinion Inference Under Adversarial Attacks
its competitive counterparts under possible adversarial attacks on the logic-rule based structured data and white and black box adversarial attacks under both clean and perturbed semi-synthetic and real-world datasets in three real world applications. The results show that the Adv-COI generates the lowest mean
Multi-robot adversarial patrolling: Handling sequential attacks
Multi-robot adversarial patrolling: Handling sequential attacks. EfratSlessLin,NoaAgmon. ∗,SaritKraus. Department of Computer Science, Bar Ilan University, Israel. a r t i c l e i n f o. a b s t r a c t. Article history: Received 12 August 2016. Received in revised form 10 February 2019. Accepted 14 February 2019. Available online 19 February
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GenAttack: Practical Black-box Attacks with Gradient-Free
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In spite of the prevalence of malicious attacks, there is no existing work studying the possibility and feasibility of malicious attacks against DRL interpretations. To bridge this gap, in this paper, we investigate the vulnerability of DRL interpretation methods. Specif-ically, we introduce the first study of the adversarial attacks against
Detecting and Mitigating Adversarial Perturbations for Robust
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the superior robustness and uncertainty handling capabili-ties of proposed models on adversarial attacks and out-of-distribution experiments for the image classiﬁcation tasks. 1. Introduction The ability of deep learning models to capture rich rep-resentations of high dimensional data has lead to successful
Adversarial Data Mining: A Game Theoretic Approach
SVM models, both wide range attacks and targeted attacks are considered and incorporated into the SVM framework. We discuss the details of our adversarial SVM models in Section 3. 2 A Game Theoretic Framework 2.1 Adversarial Stackelberg Game Assume the good class S g consists of the legitimate objects and the bad cl ass S b consists
Online Robust Policy Learning in the Presence of Unknown
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INTRIGUING PROPERTIES OF ADVERSARIAL TRAINING AT SCALE
Adversarial training is one of the main defenses against adversarial attacks. In this paper, we provide the ﬁrst rigorous study on diagnosing elements of large-scale adversarial training on ImageNet, which reveals two intriguing properties. First, we study the role of normalization. Batch Normalization (BN) is a cru-
10-708 Project Final Report: Randomized Deep Learning
In order to develop models that are robust to adversarial examples, it is necessary to know the enemy. Thus, we will ﬁrst introduce existing approaches to generate adversarial samples, followed by an analysis of current state-of-the-art models for adversarial learning. 2. Literature Review for Adversarial Attacks
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Generative Adversarial Networks (GANs): Challenges, Solutions
Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-
Multi-Robot Adversarial Patrolling: Handling Sequential Attacks
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Gradient Band-based Adversarial Training for Generalized
As adversarial attacks pose a serious threat to the security of AI system in practice, such attacks have been extensively studied in the context of computer vision applications. However, few attentions have been paid to the adversarial research on automatic path nding. In this
Defending against Adversarial Samples without Security
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Audio Adversarial Examples Generation with Recurrent Neural
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Adversarial Attacks against Intrusion Detection Systems
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Adversarial support vector machine learning
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P D : LEVERAGING GENERATIVE MODELS TO UNDERSTAND AND DEFEND
CNN generative model is very sensitive to adversarial inputs, typically giving them several orders of magnitude lower likelihoods compared to those of training and test images. Detecting adversarial examples An important step towards handling adversarial images is the ability to detect them.
POPQORN: Quantifying Robustness of Recurrent Neural Networks
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Adversarial Defense by Stratified Convolutional Sparse Coding
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Towards a Scalable and Robust DHT - TUM
dled without any problems, but handling adversarial peers is very challenging. The biggest threats appear to be join-leave attacks (i.e., adaptive join-leave behavior by the adversarial peers) and at-tacks on the data management level (i.e., adaptive insert and lookup attacks by the adversarial peers) against which no provably robust