Tag: Deep Learning

Machine Learning, Reinforcement Learning

Exploring Transformer Model for Reinforcement Learning

MLP is widely used in RL to implement a learnable agent in a certain environment trained according to a specific algorithm. Recent works in NLP have already proved that Transformer can replace and outperform MLP in most tasks leading to expanding its utilization in areas outside of NLP such as Computer Vision. However, in RL the Transformer architecture is still not widely adopted, and agents … Continue reading Exploring Transformer Model for Reinforcement Learning

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Cybersecurity, Technology

Defence methods for image adversarial attacks

In the previous post, we reviewed some well-known methods for black-box decision-based adversarial attacks where the adversary has no knowledge about the victim model except for its discrete hard-label predictions. Thus gradient-based methods become ineffective but simple random-walk-based methods such as the Boundary Attack can still represent a threat even under these particular settings. Now that we have introduced both white and black-box attacks under … Continue reading Defence methods for image adversarial attacks

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Cybersecurity, Technology

Black-box decision-based attacks on images

In the previous post, we reviewed a series of black-box score-based adversarial attacks where the adversary has to estimate the gradient by querying the target model and retrieving the labels’ confidence score. In this post, we are going to explore the third category of black-box attacks, namely, black-box decision-based attacks. Under these settings, the only knowledge the attacker has about the model is only discrete … Continue reading Black-box decision-based attacks on images

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Cybersecurity, Technology

Black-box score-based attacks on images

In the previous post, we reviewed a series of black-box transfer-based adversarial attacks where the adversary has to generate adversarial examples against a substitute model. In this post, we are going to explore the second category of black-box attacks, namely, black-box score-based attacks. Under this setting, it is not possible to access the white-box model’s gradients. The only knowledge about the attacked model is the … Continue reading Black-box score-based attacks on images

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Cybersecurity, Technology

Black-box transfer-based attacks on images

In the previous post, we reviewed a series of white-box adversarial attacks where the adversary has full access to and knowledge of the victim model. In this post, we are going to explore the first category of black-box attacks, namely, black-box transfer-based attacks. Transfer-based attacks generate adversarial examples against a substitute model, possibly being as much similar as possible to the target model, which has … Continue reading Black-box transfer-based attacks on images

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Cybersecurity, Technology

White-box adversarial attacks on images

In the first post, we introduced the concept of adversarial attacks and contextualized them in the case of images. In this post, we are going to explore the first category of attacks, namely, white-box attacks. Under this setting, the adversary has full access and knowledge of the model, that is, the architecture of the model, its parameters, gradients, and loss of respect to the input … Continue reading White-box adversarial attacks on images

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Machine Learning, Reinforcement Learning

Teaching AI to play Snake with Reinforcement Learning

It is well known that two of the most fascinating fields of computer science are gaming and artificial intelligence. The gaming field saw its origins back in the 1970s when gaming consoles such as Atari 2600, along with graphics on computer screens and home computer games were introduced to the general public giving birth to different kinds of arcade games like Pong and Pacman. In … Continue reading Teaching AI to play Snake with Reinforcement Learning

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Machine Learning, Reinforcement Learning

Introduction to Deep Reinforcement Learning

Deep Reinforcement Learning is the result of the combination of two well-known machine learning approaches: Deep Learning and Reinforcement Learning. Its main goal is the one to create a single agent able to handle any human-level task but achieving super-human results on it. A famous AI implementing this technique is AlphaGo that, in March 2016, defeated for the first time in the history a 9-dan … Continue reading Introduction to Deep Reinforcement Learning

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Machine Learning, Unsupervised Learning

Donut: unsupervised anomaly detection using VAE

In this post, we are going to use Donut, an unsupervised anomaly detection algorithm based on Variational Autoencoder which can work when the data is unlabeled but can also take advantage of the occasional labels when available. In particular, we are going to focus on detecting anomalies on time series KPIs (key performance indicators) which are time-series data, measuring metrics such as the number of … Continue reading Donut: unsupervised anomaly detection using VAE

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Machine Learning, Unsupervised Learning

GAN: Generative Adversarial Networks

Imagine a scenario where a forger attempts to produce fake currencies and the policeman has to try to distinguish those fake currencies from the real ones. At the beginning, both don’t have much experience, the forger will just come with a piece of paper with a dollar bill scribbled on it. Obviously, is that is a fake currency, but the unexperienced policeman still will struggle … Continue reading GAN: Generative Adversarial Networks

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