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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 …


MLOps lessons from Creativity, Inc. (Part 2)

Last time, we talked a bit about lean manufacturing, DevOps, MLOps, and the history of Pixar Studios according to Ed Catmull. In particular, we noted some similarities between Ed's lessons about running a film production company and MLOps best practices. In this blog post, we'll finish going through that list …


MLOps lessons from Creativity, Inc. (Part 1)

I recently finished listening to the audiobook version of Creativity Inc., Ed Catmull's book on the history of Pixar Animation. Many of the company policies and managerial decisions discussed in the book, especially the parts about experimentation and feedback, sound very similar to what you would hear in an agile …


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 …


There's treasure everywhere: a devops perspective on the Port of Long Beach

The CEO of Flexport posted a thread on Twitter last week about supply chain shortages that ended up getting a lot of attention.


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 …


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