Certifiably Robust Few-Shot Classification Against Data Poisoning Attacks in the Era of Foundation Models

Core Concepts

Our FCert is the first certified defense against data poisoning attacks to few-shot classification with foundation models. FCert provably predicts the same label for a testing input under arbitrary data poisoning attacks when the total number of poisoned support samples is bounded.

Abstract

The content presents FCert, a certified defense against data poisoning attacks to few-shot classification with foundation models.
Key highlights:

Few-shot classification with foundation models enables building accurate classifiers with a few labeled training samples, but is vulnerable to data poisoning attacks.
Existing certified defenses are designed for traditional supervised learning and achieve sub-optimal performance when extended to few-shot classification.
FCert is based on two key observations: 1) the feature vector of a testing input is close to those of support samples with the same label, and 2) under data poisoning attacks, most support samples remain unaffected.
FCert computes a robust distance between the testing input and support samples of each class by removing the largest and smallest distances. It then predicts the class with the smallest robust distance.
FCert provably predicts the same label for a testing input under arbitrary data poisoning attacks when the total number of poisoned support samples is bounded. The authors derive the certified poisoning size and prove its tightness.
Extensive experiments on benchmark datasets and foundation models demonstrate that FCert is accurate, robust, and efficient compared to state-of-the-art few-shot classification methods and certified defenses.

Stats

The content does not provide any specific metrics or figures. It focuses on the technical details of the proposed FCert defense.

Quotes

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Key Insights Distilled From

FCert: Certifiably Robust Few-Shot Classification in the Era of Foundation Models

by Yanting Wang... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08631.pdf

Deeper Inquiries

How can the certified robustness guarantee of FCert be extended to other few-shot learning algorithms beyond linear probing

The certified robustness guarantee of FCert can be extended to other few-shot learning algorithms beyond linear probing by adapting the key insights and methodologies used in FCert. One approach is to incorporate the robust distance calculation and optimization techniques used in FCert into the training and prediction process of other few-shot learning algorithms. For example, in the case of ProtoNet, which creates prototypes for each class based on support samples, the feature vectors of the prototypes can be used to compute distances between the prototypes and the testing input. By applying the principles of robust distance calculation and optimization, a certified robustness guarantee can be established for ProtoNet as well. Similarly, for other few-shot learning algorithms, the concept of comparing feature vectors and deriving robust distances can be integrated to provide certified defenses against data poisoning attacks.

What are the potential limitations of the assumptions made in the threat model, and how can the defense be further strengthened to handle more realistic attack scenarios

The assumptions made in the threat model of FCert may have limitations in capturing the full spectrum of potential attack scenarios in real-world settings. One limitation is the assumption that the attacker has complete knowledge of the few-shot classification system, including the foundation model and the classification algorithm. In reality, attackers may have varying degrees of knowledge and may employ more sophisticated strategies to evade detection. To strengthen the defense against more realistic attack scenarios, additional layers of security measures can be implemented. This may include incorporating anomaly detection techniques to identify unusual patterns in the data, implementing dynamic defense mechanisms that adapt to evolving attack strategies, and integrating multi-layered defenses to mitigate the impact of successful attacks.

Can the core ideas behind FCert be applied to provide certified defenses for other machine learning tasks beyond few-shot classification, such as few-shot object detection or few-shot segmentation

The core ideas behind FCert can be applied to provide certified defenses for other machine learning tasks beyond few-shot classification, such as few-shot object detection or few-shot segmentation. The key concept of computing robust distances between feature vectors of support samples and testing inputs can be extended to tasks that involve object detection or segmentation. For few-shot object detection, the feature vectors extracted from the objects in support samples can be compared with the feature vectors of the testing input to determine the robust distance and predict the label. Similarly, in few-shot segmentation tasks, the feature vectors of segmented regions can be used to calculate robust distances and make predictions. By adapting the principles of FCert to these tasks, certified defenses can be developed to enhance the security and robustness of machine learning models in various applications.