![]() Behavioral detection: If you notice many requests to your system with highly similar but nonidentical inputs, this is an indication of an attacker developing an adversarial example. ![]() Instead, the model should return only its highest confidence prediction, i.e. If your model is classifying 10 separate classes, and you return detailed confidence scores for every class, this is a great help in creating targeted adversarial examples. Minimize feedback: Mainly, do not return full model scores in your responses and ensure that error messages do not reveal unnecessary information.This usually requires many requests sent to your model, and there are a number of steps that can be taken to limit this access: Lastly, the ability to test adversarial examples on models that have been put in production is also very valuable for developing attacks. If it’s known that images are downscaled to a certain size and transformed to greyscale before they are input to the neural network, attackers can use this information to create more robust adversarial examples. You might be using publicly or commercially available datasets, such as imagenet, to which the attackers also have access.Īnother aspect is the processing chain the input data travels through before it’s passed to the model. The same thing applies to the data used to train the model. For example, if attackers know your model is built on open source libraries such as scikit-learn or pytorch, they can quickly set up an accurate replica. If attackers know enough details about your model, it allows them to build a replica model and develop accurate adversarial examples before they even interact with your system. These details should not be shared outside of your organisation. ![]() Obfuscation applied to adversarial machine learning means that you should consider all details surrounding your machine learning model and its entire data processing chain as highly confidential. It only requires more skill, effort and time. However, skilled reverse engineers will still be able to decompile that code and understand exactly what it is doing. Method and variable names are changed to meaningless tokens, making the code unreadable. For example, the source code of apps published on the Google Play store is often obfuscated. In practice, it will only serve to slow attackers down. Obfuscation is not a very effective defense mechanism. The less information an attacker has, the more difficult it is to locate the weak points of an IT system. By masking or shielding parts of your IT system from possible attackers, you make it more difficult for them to develop attacks. The least effective method we’ll discuss is a well-known concept in cybersecurity: obfuscation. Your neural networks after reading this post. There are different approaches to solve this issue, and we discuss them in order of least to most effective: target concealment, data preprocessing and model improvement.īecause this post mainly contains technical recommendations, we decided to improve it with GIFs from one of the best TV shows ever made. Following our accounts of what adversarial machine learning means and how it works, we close this series of posts by describing what you can do to defend your machine learning models against attackers.
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