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How Nightshade Works

Confusing image-generating AI with poisoned data

Just like the high walls of a castle, Nightshade can be a way to defend intellectual properties against illegitimate use. Photo by Nabih El Boustani on Unsplash

The recent emergence of Nightshade, an algorithm that allows to create poisoned data for confusing image-generating AI models, has given new life to the discussion on adversarial attacks on such models. This discussion is also influenced by ethical and social considerations, as such attacks may provide a way for artists, content creators, and others to fight back if they feel treated unjustly by the fact that AI models use their content without permission, but could also be used with bad intentions.

In this article, I want to explain the core concepts of Nightshade. To this end, I will first explain the general idea of data poisoning and highlight its shortcomings. Then I will introduce you to Nightshade, an algorithm that overcomes some of the disadvantages of the naive approach. In the end, I will briefly discuss some ethical considerations that come from its use.

Data poisoning

Poisonous or not? Photo by Fiona Smallwood on Unsplash

Let’s start with the idea of data poisoning in general. Say you want to influence image-generating AIs in such a way, that they fail to generate certain kinds of images, or are unable to understand certain prompts. Why do you want to do this? The most likely non-destructive reasons might be, that you are an artist and want to avoid that an image-generating model is able to generate images in your style, or that you have created a new comic character that should not be reproduced by an image-generating model without your permission.

So, what do you do? Let us start with understanding a basic concept of how generative AI learns. Of course, an image-generating AI depends on its training data. To be precise, it relies on the fact that there are images showing a certain concept (say a dog) and that those images are associated with a text describing their content (e.g. an image caption like a cute dog with glasses). From that, it learns to extract certain visual properties that images share, which also share certain keywords in their captions. That is, the model learns what dogs look like by learning the properties of all those images that mention dog in their caption.

Now, what would happen if you would introduce images that show dogs, but whose captions always mention cats? In the end, dog and cat are just symbols for what can be seen in the images. If the images that show dogs are labeled as cats, the model will just learn that the symbol cat refers to what we would call dog. Without any prior knowledge of the English language, how would the model know that the labels are wrong if they are so consistent? If you don’t speak German, and I would show you a hundred images of dogs and tell you their label is Katze, you would assume that Katze is the German word for dog. You wouldn’t know that the actual German word for dog is Hund, and Katze means cat because you just learned the correlation between the labels and the images’ properties.

The process just described is called data poisoning, stemming from the idea that you introduce data instances, that have a malicious effect on the model’s training (just like a poison has a malicious effect on your health).

Naive poisoning attacks

A cute dog with glasses, thinking about how to attack an image-generating model. Photo by Jamie Street on Unsplash

As a naive approach, you could take the aforementioned idea and use it to confuse machine learning models like Stable Diffusion. Say you want to make Stable Diffusion create images of cats when being prompted for dogs. For that, you would need to create many images of cats, label them as dogs, and upload them to the internet. Then you hope that those images are scraped for the next training of a Stable Diffusion model.

If many of your images become part of the next training run, that could indeed lead to confusion between cats and dogs. However, there are some drawbacks to that approach:

You will need many images. Since there are many other images of cats that are not poisoned, you need a large number of images to have an impact at all. If you provide only 10 poisoned images, and there are 1000 non-poisoned images of cats on the other side, you almost have no influence on the training. Typically, you can expect to poison 20% or more of all images to have an effect.Note that you do not know which images exactly will be part of the training. Hence, if you want to introduce 500 poisoned images into the training, you may have to create 5000 and spread them all over the internet, because only some of them may actually be scraped for training.If you upload images of cats, labeled as dogs, humans can easily detect that. Before using your images for training, they may be filtered out by a quality gate (being a human or a specialized AI).

Nightshade

The nightshade algorithm has its name from a very poisonous plant. Photo by Georg Eiermann on Unsplash

Now let’s take a look at Nightshade, an algorithm that aims at overcoming those disadvantages. For that, Nightshade uses two key concepts: It creates images that have the maximum effect on the model (which leads to a need for fewer images in total) and that are indistinguishable from non-poisoned images for humans.

First, how to get the maximum effect out of the images? In theory, you would want to use those images, that lead to the biggest change of the gradient during the training. However, to find out which images those are, you would have to observe the training process, which you can’t do, in general. The authors of Nightshade propose a different solution though: You take an image, that has been generated by the model you want to poison. That is, if you want to have cat images labeled as dogs, you prompt the model with a simple prompt like an image of a cat. The image it creates will be a very typical representation of what the model understood to be a cat. If this image is seen in training, it will have a very high influence on the understanding of the concept cat (a much higher than rather untypical images of cats have). Hence, if you poison that image, you will get a very large effect on the model’s training.

Second, we said that Nightshade’s images should be indistinguishable from non-poisoned images. To reach this goal, Nightshade takes natural images and applies a perturbation (i.e. a small change in the pixel’s values), until the image is perceived differently by the model. Continuing with our dog vs. cat example from above, we would take an image generated by the model that shows a cat. This image we refer to as the anchor image or xᵃ in the upcoming formulas. Next, we take a very typical image of a dog, which we refer to as xₜ. To this image xₜ, we now add the perturbation δ s.t. it optimizes the following objective:

where F() is the image feature extractor used by the model, Dist is a distance function and p is an upper bound for the δ, to avoid the image changing too much. That means we want to find δ s.t. the distance between the features of the perturbated dog image (F(xₜ + δ)) and the anchor image (showing the cat, F(xᵃ)) is as small as possible. In other words, we want the two images to look alike from the model’s perspective. Be aware, that F(x), the result of the feature extractor, is how the model sees the image in feature space, which is different from how you see the image (in pixel space, if you want).

In the following images, you won’t be able to spot any difference between the original and the poisoned images (at least I can’t). However, in their feature space, they differ a lot. The features of the poisoned dog image, for example, are very close to the features of a cat image and hence for the model it almost looks like a cat.

Two examples of poisoned images. The images in the lower line are perturbated versions of the upper ones. Although no difference can be seen by a human, the original and poisoned images look very different from a model’s perspective. Image taken from the Nightshade paper[1].

With this technique, we are able to generate images that have a very big effect on the model’s training and that can’t be detected as being poisoned. If you would upload these images to the internet, no human would be suspicious at all and hence it is very unlikely, that they would be filtered out by any quality gate. In addition, since they are so powerful, you don’t need to poison 20% of all dog images in the training data, as you would with the naive approach. With Nightshade, 50 to 100 images are typically enough to ruin a model’s performance on a particular concept.

Generalizability

Beyond the points we just saw, Nightshade has another interesting advantage, which is its ability to generalize in multiple ways.

First of all, poisoning a certain keyword also influences concepts that are related in a linguistic or semantic fashion. E.g. poisoning images of the concept dog also influences keywords like puppy or husky, which are related to dog. In the following examples, the concept dog has been poisoned and this also impedes the generation of puppies and huskies.

An example of how poisoning a concept (dog) also impedes the generation of related concepts (puppy, husky, wolf). Image taken from the Nightshade paper[1].

In a likewise fashion, poisoning a concept such as fantasy also influences concepts that are related semantically, but leaves other concepts unaffected, as can be seen in the following example. As you see, a concept like dragon, which is close to the poisoned fantasy, is affected, while a concept like chair is not.

An example of how poisoning a concept (fantasy) also impedes related concepts (e.g. dragon). Note that non-related concepts (e.g. chair) are not affected. Image taken from the Nightshade paper[1].

In addition, when poisoning multiple concepts, the ability to generate images may break down in its entirety. In the following example, 100, 250, or 500 concepts have been poisoned. With more concepts being poisoned, the generation of other concepts, that have not been poisoned at all (like person or painting in the example), is heavily impeded as well.

An example is how poisoning many concepts impedes the ability to generate images in general. Note that the concepts person, painting, and seashell have not been poisoned specifically. Image taken from the Nightshade paper[1].

In addition to that, Nightshade’s effects also generalize over different target models. Remember that we used the model we wanted to attack to generate the anchor images, that helped us construct our poisoned images. The idea behind that was, that those images are very prototypical and hence would have a strong influence on the training. We also needed access to the feature extractor to optimize the perturbation. Naturally, Nightshade’s influence is strongest if these anchor images are generated by the model that is to be attacked and if that model’s feature extractor can be used for the optimization. However, even if anchor images and feature extractor come from another model, the poisoning works quite well. That is, you could generate your poisoned images with the help of, say, Stable Diffusion 2, even if you want to attack Stable Diffusion XL. This may be of interest if you don’t have access to the model you actually want to attack.

Ethical Concerns

So far, I introduced Nightshade as a method that can be used by content creators to defend their intellectual properties against illegitimate use. However, as there are always two sides to a coin, data poisoning can as well be used in a harmful manner, may it be on purpose or not. Needless to say, data poisoning can be used to deliberately disturb generative AI models, cause financial damage to their creators, and impede scientific research. An AI company destroying the training data of their competitors to improve their own model in contrast is only one of countless examples of malign usages of data poisoning. However, even if you just want to defend your own content, we just saw, that poisoning many concepts impedes the AI’s ability to generate images in total. Hence, if many people make use of Nightshade, this may destroy image-generating AIs even on those concepts that would be legitimate to use. Hence, even with the intention to protect their own content, a content creator using Nightshade may cause unwanted damage. Whether and to which extent such collateral damage has to be accepted is a question for a vivid open debate.

On top of that, as you can imagine, attacking the capabilities of generative AI is a battle of constant ups and downs. Whenever new attacks are invented, the other side comes up with new defense mechanisms. Although the authors claim that Nightshade is quite robust against common defense mechanisms (e.g. detecting images as being poisoned by a specialized classifier or other properties), it may only be a matter of time until new defenses are discovered that counteract Nightshade. From that perspective, Nightshade may allow creators to protect their content for the time being but may become outdated sooner or later.

Summary

As we just saw, Nightshade is an algorithm to create poisoned datasets, that goes beyond the naive approach of labeling data with incorrect labels. It creates images that are not detectable as being poisoned by humans and that can influence an image-generating AI heavily even with a low number of examples. This drastically increases the chance of the poisoned images becoming part of the training and having an effect there. Even more, it promises to generalize in a number of ways, which makes the attacks more powerful and less easy to defend against. With that, Nightshade provides a new way of fighting back against the illegitimate use of content for model training, for which no permission has been given by its creators, but also includes the potential of destructive use and hence calls for a debate on its ethical implications. Being used with noble intentions, Nightshade can help defend intellectual properties such as an artist’s style or inventions though.

Sources

That is the original paper introducing Nightshade:

[1] Shan, S., Ding, W., Passananti, J., Zheng, H., & Zhao, B. Y. (2023). Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models. arXiv preprint arXiv:2310.13828.

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How Nightshade Works was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

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