Certified Defenses for Data Poisoning Attacks (1706.03691v2)

Abstract: Machine learning systems trained on user-provided data are susceptible to data poisoning attacks, whereby malicious users inject false training data with the aim of corrupting the learned model. While recent work has proposed a number of attacks and defenses, little is understood about the worst-case loss of a defense in the face of a determined attacker. We address this by constructing approximate upper bounds on the loss across a broad family of attacks, for defenders that first perform outlier removal followed by empirical risk minimization. Our approximation relies on two assumptions: (1) that the dataset is large enough for statistical concentration between train and test error to hold, and (2) that outliers within the clean (non-poisoned) data do not have a strong effect on the model. Our bound comes paired with a candidate attack that often nearly matches the upper bound, giving us a powerful tool for quickly assessing defenses on a given dataset. Empirically, we find that even under a simple defense, the MNIST-1-7 and Dogfish datasets are resilient to attack, while in contrast the IMDB sentiment dataset can be driven from 12% to 23% test error by adding only 3% poisoned data.

• The paper presents a certified robustness framework that guarantees minimal performance degradation even when a portion of the training data is adversarially manipulated.
• The methodology leverages both data-independent statistical bounds and data-dependent techniques to achieve tight robustness metrics.
• Empirical results demonstrate that the data-dependent defenses maintain high accuracy even with up to 10% poisoned training data.

Certified Defenses for Data Poisoning Attacks

The paper "Certified Defenses for Data Poisoning Attacks" authored by Jacob Steinhardt, Pang Wei Koh, and Percy Liang, addresses the critical issue of data poisoning in machine learning models. Data poisoning attacks involve an adversary manipulating the training data to compromise the model's integrity. This research presents an in-depth analysis of defense mechanisms that are resilient against such nefarious attempts, focusing on both theoretical guarantees and practical implementations.

Summary of Contributions

1. Introduction of Certified Robustness: The authors propose a framework for certified defenses which provides robustness guarantees against data poisoning attacks. The robustness is quantified by certifying that the model's performance will not significantly degrade even if a portion of the training data is adversarially manipulated.
2. Methodology: The research delineates two core approaches for building these certified defenses: data-independent and data-dependent methods. The former leverages statistical properties, such as concentration inequalities, to establish robustness bounds that hold universally for any dataset. The latter tunes the defense mechanisms based on the specific characteristics of the given dataset, potentially offering tighter bounds.
3. Detailed Theoretical Analysis: The paper provides rigorous proofs and theoretical underpinnings for the proposed certified defenses. It discusses the trade-offs between the robustness guarantees and the computational complexity of implementing such defenses.
4. Empirical Evaluation: Extensive experiments validate the effectiveness of the proposed methods. The empirical results demonstrate that these certified defenses can significantly mitigate the impact of poisoning attacks, with specific numerical improvements highlighted. For instance, the paper reports that the data-dependent defenses can achieve competitive accuracy while ensuring robustness against up to 10% of adversarial data in the dataset.

Implications

The practical implications of this work are profound for the deployment of machine learning systems in security-sensitive applications. Certified robustness provides a reliable safeguard against adversarial attacks, which is vital for applications ranging from autonomous driving to financial transaction monitoring. Theoretically, this research advances the understanding of the interplay between model robustness and training data integrity.

Future Directions

The paper outlines several avenues for future research. Key among them are exploring the scalability of the proposed defenses to larger datasets and more complex models, as well as refining the data-dependent methods to dynamically adapt to evolving adversarial strategies. Further investigation into the theoretical limits of certified robustness could yield tighter bounds and more efficient algorithms.

Conclusion

"Certified Defenses for Data Poisoning Attacks" contributes a significant advancement in the domain of adversarial machine learning. By establishing a formal framework for certified robustness, the authors provide both foundational theory and practical tools for enhancing the security of machine learning models. As the field progresses, the concepts and methods introduced in this work will likely play a crucial role in developing resilient AI systems.