Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks
This provides a forensic tool for machine learning practitioners to mitigate and prevent future data poisoning attacks, though it is incremental as it builds on existing defense and unlearning methods.
The paper tackles the problem of tracing back data poisoning attacks in neural networks by developing a forensic tool that identifies poisoned training samples with high accuracy, achieving over 98.4% precision and 96.8% recall across various attack types.
In adversarial machine learning, new defenses against attacks on deep learning systems are routinely broken soon after their release by more powerful attacks. In this context, forensic tools can offer a valuable complement to existing defenses, by tracing back a successful attack to its root cause, and offering a path forward for mitigation to prevent similar attacks in the future. In this paper, we describe our efforts in developing a forensic traceback tool for poison attacks on deep neural networks. We propose a novel iterative clustering and pruning solution that trims "innocent" training samples, until all that remains is the set of poisoned data responsible for the attack. Our method clusters training samples based on their impact on model parameters, then uses an efficient data unlearning method to prune innocent clusters. We empirically demonstrate the efficacy of our system on three types of dirty-label (backdoor) poison attacks and three types of clean-label poison attacks, across domains of computer vision and malware classification. Our system achieves over 98.4% precision and 96.8% recall across all attacks. We also show that our system is robust against four anti-forensics measures specifically designed to attack it.