# How I prepared an Elite Super Spartan Race

There’s nothing mediocre about this middle distance race. The Spartan Super offers the ideal blend between distance and speed. Offering racers a true athletic test. If you consider yourself a more seasoned athlete determined to push beyond excuses, you just might have the mettle for a Spartan Super. Serving up 25+ Spartan Obstacles and 10+km of rugged terrain, the Spartan Super spares no one. Developed … Continue reading How I prepared an Elite Super Spartan Race

# Min-max in array

Given an array v of n numbers, where n=2k, k>0 be a natural number, find the minimum and maximum element in v. There are no assumptions regarding the orders of the elements of v. A basic iterative approach would require 2(n-1) comparisons (n-1 comparisons to find the minimum and n-1 comparisons to find the maximum). However, your program MUST perform at most 3/2n-1 comparisons. Difficulty: … Continue reading Min-max in array

# K-th smallest element in unsorted array

Given an array v of distinct numbers, and a number k where k is smaller than the size of v, find the k-th smallest element in the array. A simple solution would be first sorting the array in growing order and than selecting the k-th element which can be done in θ(n logn). However, your program MUST run in θ(n) on average. Difficulty: Medium. Input There … Continue reading K-th smallest element in unsorted array

# Adversarial policies: attacking TicTacToe multi-agent environment

In a previous post we discussed about the possibility for an attacker to fool image classification models by injecting adversarial noise directly to the input images. Similarly, in this post we are going to see how is it possible to attack deep reinforcements learning agents on multi-agent environments (where two or more agents interact within the same environment) such that one or more agents are … Continue reading Adversarial policies: attacking TicTacToe multi-agent environment

# 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

# Introduction to adversarial attacks on images

Nowadays, image classification deep learning models are always more present in our systems in order to create smarter applications or simply to replace human operators to automatically perform some repetitive tasks. Their increased utilization is due to their high accuracy such that recent models are now able to outperform humans’ brain in many object classification tasks. However, despite their good generalization, deep neural networks are … Continue reading Introduction to adversarial attacks on images