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 … Continue reading Defence methods for image adversarial attacks

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 this settings, the only knowledge the attacker has about the model are only discrete … Continue reading Black-box decision-based attacks on images

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 to the white-box model’s gradients. The only knowledge about the attacked model are … Continue reading Black-box score-based attacks on images

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 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 have a … Continue reading Black-box transfer-based attacks on images

White-box adversarial attacks on images

In the first post we introduced the concept of adversarial attacks and contextualized 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, it’s parameters, gradients and loss respect to the input as well … Continue reading White-box adversarial attacks on images

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

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

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

Balancing a cart pole with policy gradients algorithm

In this post we are going to analyze a type of reinforcement learning algorithm called policy gradients. In the field of reinforcement learning, we have an agent making observations and taking actions within an environment in order to receive some rewards and its main objective is to learn a policy such that its actions will maximize its expected long-term rewards. In this case, our agent … Continue reading Balancing a cart pole with policy gradients algorithm

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