Archives for adversarial learning

01 Aug

How To Confuse a Neural Network Using Fast Gradient Sign Method?

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image-24777

Many machine learning models, including neural networks, consistently misclassify the adversarial examples. Adversarial examples are nothing but specialised inputs created to confuse neural networks, ultimately resulting in misclassification of the result. These notorious inputs are almost the same as the original image to human eyes but cause a neural network to fail to identify the image’s content.

The post How To Confuse a Neural Network Using Fast Gradient Sign Method? appeared first on Analytics India Magazine.

11 Mar

Explained: MIT Scientists’ New Reinforcement Learning Approach To Tackle Adversarial Attacks

Adversarial inputs, also known as machine learning’s optical illusions, are inputs to the model an attacker has intentionally designed to confuse the algorithm into making a mistake. Such inputs can be typically dangerous for machines with a very low margin for risk. For instance, in self-driving cars, an attacker could target an autonomous vehicle with…

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13 Jul

How To Secure Deep Learning Models From Adversarial Attacks

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image-13989

With recent advancements in deep learning, it has become critical to improve the robustness of the deployed algorithms. Vulnerability to adversarial samples has always been a critical concern while implementing these DL models for safety-critical tasks like autonomous driving, fraud detection, and facial recognition. Such adversarial inputs are usually undetectable to the human eye. However,…

The post How To Secure Deep Learning Models From Adversarial Attacks appeared first on Analytics India Magazine.

24 Jun

Top 8 Adversarial Methods For Transfer Learning

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image-13486

Adversarial learning is one of the most promising ways to train and secure robust deep learning networks. Transfer learning is a critical approach that enables training deep neural networks (DNN) faster and with a relatively lesser amount of data than training from scratch. In this article, we list down the top 8 Adversarial Methods one…

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18 Jun

Top 12 Papers On Adversarial Learning At CVPR 2020

Security in data science practices has always been one of the crucial concerns among organisations. With the increase of using machine learning and deep learning models, researchers have been trying to make these models secure and robust in every way possible. Adversarial learning helps in improving the performance of machine learning systems.  Below here we…

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04 Mar

Adversarial Attacks That Can Corrupt Reinforcement Learning Systems

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image-10510

The age of algorithmic innovations has now entered a new realm where the researchers are finding flaws in the techniques through adversarial attacks. In the case of computer vision problems, the role of adversarial attacks has been well established, and there have been several startups that are concentrating only on adversarial attacks.  Any talk of…

The post Adversarial Attacks That Can Corrupt Reinforcement Learning Systems appeared first on Analytics India Magazine.