Results 31 to 40 of about 156,834 (158)
Are Accuracy and Robustness Correlated?
, 2016 Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors.
Boult, Terrance E. +2 more
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Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains
, 2017 While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication
Kantardzic, Mehmed, Sethi, Tegjyot Singh
core +1 more source
Adversarial support vector machine learning [PDF]
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012 Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack strategy and develop robust learning models to mitigate the attack. These are the two objectives of this paper.
Yan Zhou +3 more
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NUST Journal of Engineering Sciences, 2022 The availability of information and its integrity and confidentiality are important factors in information and communication of the system security. The DDoS attack generally means Distributed denial of services generates many enormous packets to slow ...
Zahid Iqbal +3 more
doaj +1 more source
Adversarial attacks on medical machine learning
Science, 2019 Emerging vulnerabilities demand new ...
Finlayson, Samuel G. +5 more
openaire +4 more sources
Adversarial Machine Learning in Text Processing: A Literature Survey
IEEE Access, 2022 Machine learning algorithms represent the intelligence that controls many information systems and applications around us. As such, they are targeted by attackers to impact their decisions.
Izzat Alsmadi +11 more
doaj +1 more source
Adversarial Controls for Scientific Machine Learning
ACS Chemical Biology, 2018 New machine learning methods to analyze raw chemical and biological data are now widely accessible as open-source toolkits. This positions researchers to leverage powerful, predictive models in their own domains. We caution, however, that the application of machine learning to experimental research merits careful consideration.
Kangway V. Chuang, Michael J. Keiser
openaire +4 more sources
Breaking Machine Learning Models with Adversarial Attacks and its Variants
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceMachine learning models can be by adversarial attacks, subtle, imperceptible perturbations to inputs that cause the model to produce erroneous outputs.
Pavan Reddy
doaj +1 more source
Investigation of the impact effectiveness of adversarial data leakage attacks on the machine learning models [PDF]
ITM Web of ConferencesMachine learning solutions have been successfully applied in many aspects, so it is now important to ensure the security of the machine learning models themselves and develop appropriate solutions and approaches.
Parfenov Denis +3 more
doaj +1 more source
Hardening quantum machine learning against adversaries
New Journal of Physics, 2018 Security for machine learning has begun to become a serious issue for present day applications. An important question remaining is whether emerging quantum technologies will help or hinder the security of machine learning. Here we discuss a number of ways that quantum information can be used to help make quantum classifiers more secure or private.
Nathan Wiebe, Ram Shankar Siva Kumar
openaire +3 more sources