https://tomroth.dev/tags/adversarial/Recent content in adversarial on Tom RothHugo -- gohugo.ioenFri, 06 Mar 2020 00:00:00 +0000https://tomroth.dev/invariance/Fri, 06 Mar 2020 00:00:00 +0000https://tomroth.dev/invariance/Sensitivity-based adversarial attacks Link to heading Neural networks are vulnerable to adversarial attack, and there’s a lot of talk about this. There are many definitions of an adversarial attack, but let’s go with this one: an input to a model designed to produce an incorrect output. Models can run on many different types of data (text, audio, tabular) but let’s use images as a working example. The most well-known kind of adversarial attack is to add some kind of perturbation to an image to make a classifier mislabel it.https://tomroth.dev/deepfool/Thu, 27 Feb 2020 00:00:00 +0000https://tomroth.dev/deepfool/Paper: Deepfool: a simple and accurate method to fool deep neural networks. (Moosavi-Dezfooli et al, 2016). Idea: If you want to find an adversarial example to an image, look for the closest decision boundary, and then move the image towards a linear approximation of the decision boundary by orthogonally projecting it onto the boundary. Once it crosses the boundary it will be an adversarial image. Background Link to heading Affine functions.https://tomroth.dev/ten-failed-detectors/Mon, 20 Jan 2020 00:00:00 +0000https://tomroth.dev/ten-failed-detectors/Some rough notes of this paper. Paper link: Adversarial examples are not easily detected: Bypassing ten detection methods. (Carlini and Wagner, 2017) In brief: Adversarial defences are often flimsy. The authors are able to bypass ten detection methods for adversarial examples. They do so with both black-box and white-box attacks. The C&W attack is the main attack used. The most promising defence evaluated the classification uncertanity of each image through generating randomised models.