Attacking machine learning with adversarial examples
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post w
To give an example of how a simple defense can fail, let’s consider why a technique called “gradient masking” does not work.
Gradient masking” is a term introduced in _Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples_(opens in a new window). to describe an entire category of failed defense methods that work by trying to deny the attacker access to a useful gradient.
Most adversarial example construction techniques use the gradient of the model to make an attack. In other words, they look at a picture of an airplane, they test which direction in picture space makes the probability of the “cat” class increase, and then they give a little push (in other words, they perturb the input) in that direction. The new, modified image is mis-recognized as a cat.
But what if there were no gradient — what if an infinitesimal modification to the image caused no change in the output of the model? This seems to provide some defense because the attacker does not know which way to “push” the image.
We can easily imagine some very trivial ways to get rid of the gradient. For example, most image classification models can be run in two modes: one mode where they output just the identity of the most likely class, and one mode where they output probabilities. If the model’s output is “99.9% airplane, 0.1% cat”, then a little tiny change to the input gives a little tiny change to the output, and the gradient tells us which changes will increase the probability of the “cat” class. If we run the model in a mode where the output is just “airplane”, then a little tiny change to the input will not change the output at all, and the gradient does not tell us anything.
Let’s run a thought experiment to see how well we could defend our model against adversarial examples by running it in “most likely class” mode instead of “probability mode.” The attacker no longer knows where to go to find inputs that will be classified as cats, so we might have some defense. Unfortunately, every image that was classified as a cat before is still classified as a cat now. If the attacker can guess which points are adversarial examples, those points will still be misclassified. We haven’t made the model more robust; we have just given the attacker fewer clues to figure out where the holes in the models defense are.
Even more unfortunately, it turns out that the attacker has a very good strategy for guessing where the holes in the defense are. The attacker can train their own model, a smooth model that has a gradient, make adversarial examples for their model, and then deploy those adversarial examples against our non-smooth model. Very often, our model will misclassify these examples too. In the end, our thought experiment reveals that hiding the gradient didn’t get us anywhere.
The defense strategies that perform gradient masking typically result in a model that is very smooth in specific directions and neighborhoods of training points, which makes it harder for the adversary to find gradients indicating good candidate directions to perturb the input in a damaging way for the model. However, the adversary can train a substitute model: a copy that imitates the defended model by observing the labels that the defended model assigns to inputs chosen carefully by the adversary.
Authors
Ian Goodfellow, Nicolas Papernot, Sandy Huang, Yan Duan, Pieter Abbeel, Jack Clark