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Showing 1 to 17 of 17 for “"Adversarial Example"”.
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DETECTING AND DEFENDING AGAINST DIFFERENT FAMILIES OF ADVERSARIAL EXAMPLE ATTACKS
Adversarial example attacks alter an image so the image appears largely unaltered to human eyes, but image-recognition models will misclassify it. This is a common type of attack, against which there is currently no good general defense. Most state-of-the-art methods of detecting adversarial …
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On Transferability of Adversarial Examples on Machine-Learning-Based Malware Classifiers
… vulnerable and sensible to transferable adversarial example (AE) attacks. The transfer AE attack does not require extra information from the victim model such as gradient information. Researchers explore mainly 2 lines of transfer-based adversarial example attacks: ensemble models and …
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Understanding Adversarial Training: Improve Image Recognition Accuracy of Convolution Neural Network
… neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence.</p> <p>The main …
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Comparing learned representations of deep neural networks
… geometry. We also study connections to adversarial examples and observe that networks with more similar hidden representation geometries also exhibit higher rates of adversarial example transferability.
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Evaluating robustness of neural networks with mixed integer programming
… However, neural networks can be fooled by adversarial examples -- slightly perturbed inputs that are misclassified with high confidence. Verification of networks enables us to gauge their vulnerability to such adversarial examples. We formulate verification of piecewise-linear neural …
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Multi-model-based defense against adversarial examples for neural networks
… models have been found to be vulnerable to adversarial examples, i.e., carefully crafted examples aiming to mislead machine learning models. Adversarial examples can pose potential risks on safety/security-critical applications. Existing defense approaches are still vulnerable to emerging …
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Building trustworthy machine learning systems in adversarial environments
… interacting with a machine-learning model. These adversarial scenarios, known as poisoning attack, adversarial example attack, and inference attack, have demonstrated that security, privacy, and robustness have become more important than ever for AI to gain wider adoption and societal trust. To …
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An evaluation of the robustness of the natural-adversarial mutual information-based defense and malware classification against adversarial attacks for deep learning
… way. These modified samples are known as adversarial examples and have been crafted with the goal of causing the target DNN to modify its behavior. It has been shown that adversarial examples can be crafted even when the attacker does not have access to the training parameters and model …
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Adversarial training objectives for generative attacks on text classifiers
In natural language processing, creating textual adversarial examples is challenging. These examples aim to deceive text classifiers into incorrect predictions while maintaining linguistic similarity to genuine inputs. This complexity arises from the need to preserve the original text's fluency, …
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Understanding Adversarial Robustness in Deep Learning
This thesis studies the adversarial robustness of deep learning models. Our investigation covers various aspects of this phenomenon, including the development of two new defense algorithms, two new attack algorithms, a novel definition of hierarchical adversarial robustness, and an analysis of how …
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ATTACK AND DEFENSE IN SECURITY ANALYTICS
… and prevents the potential threat from adversarial attacks.</p> <p>In the first part, we demonstrate case studies in solving the security problem of categorical classification and time-series abnormal detection. In the proposed framework, we handle the incoming data by utilizing the …
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Evaluating Adversarial Robustness of Detection-based Defenses against Adversarial Examples
… it has been shown that they are vulnerable to adversarial attacks, a set of techniques that violate the integrity, confidentiality, or availability of such systems. In particular, one of the most studied phenomena concerns adversarial examples, i.e., input samples that are carefully manipulated …
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Enhancing Communications Aware Evasion Attacks on RFML Spectrum Sensing Systems
… against such communications threats termed an adversarial evasion attack in which intelligently crafted perturbations of the RF signal are used to fool a DNN-enabled classifier, therefore securing the communications channel. One often overlooked aspect of evasion attacks is the concept of …
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Adversarial Attacks on Natural Language and Speech Processing Models
… deep learning models are vulnerable to adversarial attacks. A deliberate and specific perturbation of a clean input sample can create an adversarial example, which, when processed by the model, leads to incorrect predictions. Such vulnerabilities can be exploited by malicious adversaries …
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A Hardware/Software Co-integration Approach for Securing Network Infrastructure using Reconfigurable Computing
… (DL) based network intrusion detection to adversarial examples in IoT networks. Even though DL approaches have proven extremely effective in intrusion detection given the high volume of network traffic in modern IoT networks, DL models are prone to misclassification to minute perturbations …
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Towards Robust Deep Neural Networks
… to attacks, such as Trojans during training and Adversarial Examples at test time. Adversarial Examples are inputs with carefully crafted perturbations added to benign samples. In the Computer Vision domain, while the perturbations being imperceptible to humans, Adversarial Examples can …
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Some New Results in Distributed Tracking and Optimization
… in simulation-based optimization, and adversarial example generation for attacking deep neural networks. We propose a novel function value based gradient estimator, which has better variance, and better query-efficiency compared to existing estimators. The proposed estimator covers the …