Adversarial ML
Författare:
Bhavya Shah
Last Updated:
för 7 år sedan
Licens:
Creative Commons CC BY 4.0
Sammanfattning:
About Adversarial Machine Learning
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\title{Adversarial Machine LLearning} % Poster title
\author{Bhavya Shah, Parvez Faruki} % Author(s)
\institute{Government MCA College, Ahmedabad} % Institution(s)
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\begin{document}
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The field of adversarial machine learning is also useful for identification of vulnerabilities in a machine learning approach in presence of adversarial settings
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\item Illustrate the design cycle of a learning-based pattern recognition system for adversarial tasks.
\item Performance of pattern classifiers and deep learning algorithms under attack, evaluate their vulnerabilities.
\item Pattern recognition tasks like object recognition in images, biometric identity recognition, spam and malware detection.
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Deep neural networks and \textbf{machine-learning} algorithms are currently used in several applications, ranging from computer vision to computer security.
Many areas of machine learning are \textbf{adversarial} in nature because they are safety critical, such as autonomous driving. An adversary can be a cyber attacker or malware author attacking the model by causing congestion among users, or may create accidental situations, or may even model expose vulnerabilities in the prediction module by creating undesired situation. \cite{Smith:2012qr}.
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Pattern classifiers can be significantly vulnerable to well-crafted, sophisticated attacks exploiting knowledge of the learning algorithms. Being increasingly adopted for security and privacy tasks, it is very likely that such techniques will be soon targeted by specific attacks, crafted by skilled attackers. Larger number of potential attack scenarios, respectively referred to as evasion and poisoning attacks.
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\textbf{Poisoning attacks} include those systems that exploit feedback from the end users to validate their decisions.PDFRate an online tool for detecting malware in PDF files.
\textbf{Evasion attacks} consist of manipulating input data at test time to cause misclassifications. Which manipulation of malware code to have the corresponding sample undetected.
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\item Poisoning (Causative) Attack : Attack on training phase. Attackers attempt to learn, influence, or corrupt the ML model itself.
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\item Evasion (Exploratory) Attack : Attack on testing phase. Do not tamper with ML model, but instead cause it to produce adversary selected outputs.
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Images that can be misclassified by deep-learning algorithms while being only imperceptibly distorted. evasion attacks are thus already a relevant threat in real-world application settings.
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