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Smiling is all you need: fooling identity recognition by having emotions

In "Wear your sunglasses at night", we saw that you could use an accessory, like a pair of sunglasses, to cause machine learning models to misbehave. Specifically, if you have access to images that might be used to train an identity recognition model, you can superimpose barely-visible watermarks of sunglasses …


Wear your sunglasses at night : fooling identity recognition with physical accessories

In "A faster way to generate backdoor attacks", we saw how we could replace computationally expensive methods for generating poisoned data samples with simpler heuristic approaches. One of these involved doing some data alignment in feature space. The other, simpler approach, was applying a low-opacity watermark. In both cases, the …


A faster way to generate backdoor attacks

Last time, we talked about data poisoning attacks on machine learning models. These are a specific kind of adversarial attack where the training data for a model are modified to make the model's behavior at inference time change in a desired way. One goal might be to reduce the overall …


Poisoning deep learning algorithms

Up to this point, when we have been talking about adversarial attacks on machine learning algorithms, it has been specifically in the context of an existing, fixed model. Early work in this area assumed a process where an attacker had access to test examples after capture (e.g., after a …


Evading real-time detection with an adversarial t-shirt

In the last blog post, we saw that a large carboard cutout with a distinctive, printed design could help a person evade detection from automated surveillance systems. As we noted, this attack had a few drawbacks -- largely, that the design needed to be held in front of the person's body …


Evading CCTV cameras with adversarial patches

In our last blog post, we looked at a paper that used a small sticker (a "patch") to make any object appear to be a toaster to image recognition models. This is known as a misclassification attack -- the model still recognizes that there is an object there, but fails to …


Fooling AI in real life with adversarial patches

In our last blog post, we talked about how small perturbations in an image can cause an object detection algorithm to misclassify it. This can be a useful and sneaky way to disguise the contents of an image in scenarios where you have taken a digital photograph, and have the …


What is adversarial machine learning?

If you work in computer security or machine learning, you have probably heard about adversarial attacks on machine learning models and the risks that they pose. If you don't, you might not be aware of something very interesting -- that the big fancy neural networks that companies like Google and Facebook …


Getting started with timeseries data augmentation

Data augmentation is a critical component in modern machine learning practice due to its benefits for model accuracy, generalizability, and robustness to adversarial examples. Elucidating the precise mechanisms by which this occurs is a currently active area of research, but a simplified explanation of the current proposals might look like …


Installing cuda on Ubuntu 18.04 for pytorch or tensorflow

I recently needed to update some servers running an old Ubuntu LTS (Xenial, 16.04) to a slightly less old Ubuntu LTS (Bionic, 18.04). I had been putting it off for some time, mostly due to the noise I heard about problems installing the Nvidia CUDA toolkit. But that …


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