Adversarial training with restricted data manipulation
This work addresses security issues in applications like spam filtering and malware detection by making adversarial training more realistic, though it is incremental as it builds on existing pessimistic bilevel optimization approaches.
The paper tackles the problem of adversarial training becoming overly pessimistic due to unrestricted adversaries, which can lead to unrealistic data and poor real-world classifier performance, by proposing a constrained pessimistic bilevel optimization model that restricts adversary movements and shows improved average performance over existing methods.
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as spam email filtering, malware detection and fake image generation, where security methods must be actively updated to keep up with the everimproving generation of malicious data. Pessimistic Bilevel optimisation has been shown to be an effective method of training resilient classifiers against such adversaries. By modelling these scenarios as a game between the learner and the adversary, we anticipate how the adversary will modify their data and then train a resilient classifier accordingly. However, since existing pessimistic bilevel approaches feature an unrestricted adversary, the model is vulnerable to becoming overly pessimistic and unrealistic. When finding the optimal solution that defeats the classifier, it is possible that the adversary's data becomes nonsensical and loses its intended nature. Such an adversary will not properly reflect reality, and consequently, will lead to poor classifier performance when implemented on real-world data. By constructing a constrained pessimistic bilevel optimisation model, we restrict the adversary's movements and identify a solution that better reflects reality. We demonstrate through experiments that this model performs, on average, better than the existing approach.