PAD-FT: A Lightweight Defense for Backdoor Attacks via Data Purification and Fine-Tuning (2409.12072v1)

Abstract: Backdoor attacks pose a significant threat to deep neural networks, particularly as recent advancements have led to increasingly subtle implantation, making the defense more challenging. Existing defense mechanisms typically rely on an additional clean dataset as a standard reference and involve retraining an auxiliary model or fine-tuning the entire victim model. However, these approaches are often computationally expensive and not always feasible in practical applications. In this paper, we propose a novel and lightweight defense mechanism, termed PAD-FT, that does not require an additional clean dataset and fine-tunes only a very small part of the model to disinfect the victim model. To achieve this, our approach first introduces a simple data purification process to identify and select the most-likely clean data from the poisoned training dataset. The self-purified clean dataset is then used for activation clipping and fine-tuning only the last classification layer of the victim model. By integrating data purification, activation clipping, and classifier fine-tuning, our mechanism PAD-FT demonstrates superior effectiveness across multiple backdoor attack methods and datasets, as confirmed through extensive experimental evaluation.

• The paper presents PAD-FT, which extracts likely clean data using symmetric cross-entropy and fine-tunes only the classifier to mitigate backdoor threats.
• It achieves an Attack Success Rate below 0.7% on CIFAR-100 under the WaNet attack while maintaining competitive classification accuracy.
• The method removes the need for an auxiliary clean dataset and full model retraining, significantly reducing computational overhead.

A Technical Review of PAD-FT: A Lightweight Defense for Backdoor Attacks

The increasing deployment and reliance on deep neural networks (DNNs) across various domains have simultaneously exposed these models to a range of security threats, most notably, backdoor attacks. The paper "PAD-FT: A Lightweight Defense for Backdoor Attacks via Data Purification and Fine-Tuning," authored by Yukai Xu, Yujie Gu, and Kouichi Sakurai, addresses this challenge by proposing a computationally efficient defense strategy that mitigates said attacks without necessitating an auxiliary clean dataset. This work serves as a critical contribution to the literature, given the resource-intensive nature of prevailing defense techniques against backdoor threats.

Core Contributions and Methodology

The authors introduce PAD-FT, a method characterized by three principal components: data purification, activation clipping, and classifier fine-tuning. The distinguishing feature of PAD-FT lies in its avoidance of an additional clean dataset and a full model retraining phase, which are typical requirements in conventional schemes.

1. Data Purification: The method begins with a novel data purification process utilizing symmetric cross-entropy (SCE) as a selection metric to extract likely clean data from a poisoned dataset. This results in a self-purified clean dataset that forms the basis for further model adjustments.
2. Activation Clipping: Subsequently, the purified dataset is employed to calibrate activation clipping bounds. This process limits activation outputs to prevent the exaggerated node responses typically induced by backdoor triggers.
3. Classifier Fine-tuning: The final stage involves fine-tuning only the classifier of the model using the purified dataset. Unlike conventional methods that retrain the entire network, this approach reduces computational overhead significantly while retaining efficacy.

Experimental Results

The paper provides comprehensive experimental evaluations across CIFAR-10 and CIFAR-100 datasets against popular backdoor attack strategies such as BadNets, Blended, and WaNet, with poison rates set at 5% and 10%. PAD-FT consistently demonstrates lower Attack Success Rates (ASR) compared to existing methods while maintaining competitive levels of classification accuracy (ACC). For instance, in the CIFAR-100 dataset with a 10% poison rate using the WaNet attack, PAD-FT achieves an ASR below 0.7%, a substantial improvement over other defenses like DBD which reports ASR rates of 97.19% under similar conditions.

Theoretical and Practical Implications

From a theoretical standpoint, PAD-FT proposes a fresh perspective on managing poisoned data without requiring external verification, thereby removing the dependence on clean data availability. The integration of SCE for purification and targeted fine-tuning serves as a critical innovation, potentially guiding future works in optimization-focused defensive strategies.

Practically, PAD-FT offers a scalable and less resource-intensive solution, particularly advantageous for scenarios where computational resources are limited or data origins cannot be verified conclusively. Its implementation can be directly integrated into existing training pipelines and applied to a broad range of model architectures and attack scenarios.

Future Directions

This research paves the way for further exploration into lightweight and adaptive defense mechanisms. Future works can build upon the foundation established by PAD-FT, exploring the use of alternative purification metrics, optimizing clipping bounds dynamically, and expanding the method's applicability across different types of neural network models. Additionally, addressing adversarial adaptability to PAD-FT through enhanced adversarially robust training presents a compelling avenue for ongoing research.

In conclusion, PAD-FT emerges as a promising defense mechanism for backdoor attacks, balancing robust security measures with computational efficiency and practicality in implementation. Its innovative use of self-purification and minimalistic fine-tuning underscores a notable advancement in the security of DNNs, with significant implications for both theoretical research and practical deployment within the field of AI and machine learning security.