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

Perturbation Sensitivity of GNNs - Stanford University

adversarial attacks on GNNs. These attacks altered node classifications by changing a small number of edges close to a target node and proved to be quite effective, demonstrating that GNNs are quite vulnerable to malicious adversaries. These attacks could be designed to only use edges out-side the 1-hop neighborhood of a node, or even

Adversarial Defense - Stanford University

adversarial examples, ICLR 2018 Cohen, Jeremy M., Elan Rosenfeld, and J. Zico Kolter. Certified adversarial robustness via randomized smoothing, ICML 2019 Samangouei, Pouya, Maya Kabkab, and Rama Chellappa. Defense-gan: Protecting classifiers against adversarial attacks using generative models, ICLR 2018

Practitioners guide to MLOps: A framework for continuous

Handling concerns about model fairness and adversarial attacks. MLOps is a methodology for ML engineering that unifies ML system development (the ML element) with ML system operations (the Ops element). It advocates formalizing and (when beneficial) automating critical steps of ML system construction.

Poisoning Attacks in Federated Learning: An Evaluation on

enables better handling of sensitive data, e.g. of individuals, or business related content. Applications can further benefit from the distributed nature of the learning by using multiple computer resources, and eliminating network communication overhead. Adversarial Machine Learning in general deals with attacks on

Steganographic universal adversarial perturbations

frequency domain to get adversarial examples (right). Labels predicted by ResNet- 50 are also indicated. 2. Related work Adversarial attacks on DNNs provide an opportunity to estimate a network s robustness in adversarial settings before its deploy- ment in the real-world. They have recently attracted significant at-

GenAttack: Practical Black-box Attacks with Gradient-Free

GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationGECCO 19, July 13 17, 2019, Prague, Czech Republic DNN can then be attacked using any white-box technique, and the generated adversarial examples are used to attack the target DNN.

Malicious Attacks against Deep Reinforcement Learning

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

sarial attacks where the goal is to make the face recognition system perform a misclassi cation of the input. While extensive research has been conducted on evaluating the vulnerabilities to spoo ng attacks and associated counter-measures [24], handling adversarial attacks is relatively less explored in the literature.

Improving Robustness and Uncertainty Modelling in Neural

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

agents. Recent work on generating adversarial attacks have shown that it is compu-tationally feasible for a bad actor to fool a DRL policy into behaving sub optimally. Although certain adversarial attacks with specific attack models have been ad-dressed, most studies are only interested in off-line optimization in the data space


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

A Comparative Study of Autoencoders against Adversarial Attacks

generate adversarial examples from clean inputs using the formula: , where J is the cost to train the model. In this section, we study the performance of two classifiers in handling FGSM adversarial attacks without any prefiltering. In the first classifier model, we utilized the logistic regression

Def-IDS: An Ensemble Defense Mechanism Against Adversarial

against adversarial attacks. Our work has three key merits in handling the three constraints: 1) it resists both known and unknown attacks while guaranteeing the intrusion detection accuracy; 2) it enables efficient one-time retraining for the multi-class detector rather than costly retraining for every

DHS Incident Handling Overview for Election Officials

adversarial access, and detect indicators of compromise. Security Program Review: A review of the client s existing security roles, responsibilities, and policies. Digital media analysis: Technical forensic examination of digital artifacts to detect malicious activity and develop further indicators to prevent future attacks.

Combating Adversarial Misspellings with Robust Word Recognition

Following the discovery that imperceptible attacks could cause image recognition models to misclas-sify examples (Szegedy et al.,2013), a veritable sub-field has emerged in which authors iteratively propose attacks and countermeasures. For all the interest in adversarial computer vi-sion, these attacks are rarely encountered out-

Multi-Robot Adversarial Patrolling: Facing Coordinated Attacks

problem of coordinated attacks, in which the adversary initiates two attacks in order to maximize its chances of successful penetration, assuming a robot from the team will be sent to examine a penetra-tion attempt. We suggest an algorithm that computes the optimal robot strategy for handling such coordinated attacks, and show that

Randomizing SVM against Adversarial Attacks Under Uncertainty

randomized SVMs against generalized adversarial attacks under uncer-tainty, through learning a classi er distribution rather than a single clas-si er in traditional robust SVMs. The randomized SVMs have advan-tages on better resistance against attacks while preserving high accuracy of classi cation, especially for non-separable cases.

The Case of Adversarial Inputs for Secure Similarity

of the attacks by measuring their success on synthetic and real data from the areas of e-discovery and patient similarity. To mitigate such perturbation attacks we propose a server-aided architecture, where an additional party, the server, as-sists in the secure similarity approximation by handling the common randomness as private data. We

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

settings, the adversarial model and the patrol task. In Section 4 we define the reor-ganization phase, and we lay the foundations for this work in Section 5. In Section 6 we provide a polynomial optimal patrol algorithm for the case in which the time between the sequential attacks is bounded. The unbounded case is handled in Sec-

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

classification performance and robustness to attacks compared with state-of-art solutions. Index Terms Adversarial deep learning, security through obscurity, data transformation, malware detection. I. INTRODUCTION Like all other machine learning approaches, deep learning is vulnerable to what is known as adversarial samples [9].

Audio Adversarial Examples Generation with Recurrent Neural

adversarial audio examples for the target KWS systems. The LSTM, which basically consists of input gate, output gate and forget gate, is well-suited for the handling of data that involves with time or order (such as audio or video). B. Keyword Spotting System In this paper, we choose the KWS system introduced in [27] as our target model.

Adversarial Attacks against Intrusion Detection Systems

Adversarial Attacks against Intrusion Detection Systems Deep Learning is the state-of-the-art classification method used for anomaly-based intrusion detection. Recent research has revealed that Deep Learning is vulnerable to specifically crafted attacks called Adversarial Attacks

Data integrity critical in securing autonomous AI

Dealing with adversarial attacks In addition to securing the ML data at rest, organisations need to be aware of more subtle issues inherent in ML learning process that can be exploited by adversaries: such as poisoning or disrupting existing telemetry feeds, and supplying false data. Perhaps the most prominent

US Policy Response to Cyber Attack on SCADA Systems

Nov 20, 2017 threat landscape, especially with observed adversarial and criminal activity throughout our domestic national infrastructure. Actors, motivations, and techniques range widely, yet the potential for significant consequences is un­ deniable. The president and the interagency community have made great

Decoding the Imitation Security Game: Handling Attacker

Adversarial Machine Learning. Previous work on adversarial learning has investigated various types of attacks to machine learning algorithms in various learning domains [1, 9, 13, 14, 25]. Prediction accuracy is the main measure used in existing work. In particular, the learner attempts to find a robust learning algorithm which maxi-

University of Louisville ThinkIR: The University of

an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic

Incident Handling Elections - Homepage CISA

INCIDENT HANDLING COMMON MISTAKES. WE ALL MAKE MISTAKES. BUT BEING AWARE OF COMMON ONES BEFORE AN INCIDENT OCCURS HELPS US AVOID THEM. ATTEMPTING TO MITIGATE impacts to the affected systems before incident responders can protect and recover data Doing so can cause the loss of volatile data, such as memory and other host-based artifacts

Adversarial support vector machine learning

adversarial learning, attack models, robust SVM 1. INTRODUCTION Many learning tasks, such as intrusion detection and spam ltering, face adversarial attacks. Adversarial exploits cre-ate additional challenges to existing learning paradigms. Gen-eralization of a learning model over future data cannot be


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

Adversarial attacks in RNNs. Crafting adversarial ex-amples of RNNs in natural language processing and speech recognition has started to draw public attentions in addition to the adversarial examples of feed-forward networks in im-age classifications. Adversarial attacks of RNNs on text clas-sification task (Papernot et al.,2016a), reading

Adversarial Defense by Stratified Convolutional Sparse Coding

method is far more capable of effectively handling a variety of image resolutions, large and small image perturbations, and large-scaled datasets. Among image-transformation-based adversarial de-fenses, our image projection onto quasi-natural image space achieves the best blend of image detail preserva-

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