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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:… Read more Continue reading Min-max in array
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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… Read more Continue reading K-th smallest element in unsorted array
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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… Read more Continue reading Adversarial policies: attacking TicTacToe multi-agent environment
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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… Read more Continue reading Defence methods for image adversarial attacks
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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… Read more Continue reading Black-box decision-based attacks on images
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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… Read more Continue reading Black-box score-based attacks on images
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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… Read more Continue reading Black-box transfer-based attacks on images
