Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models

TL;DR

This paper introduces Deferred Activated Backdoor Functionality (DABF), a new stealthy backdoor attack that remains hidden until after model updates, exploiting model fine-tuning to evade detection.

Contribution

The paper proposes DABF, a novel backdoor attack paradigm that conceals malicious behavior until after model retraining, along with a two-stage training scheme called DeferBad.

Findings

DABF effectively evades existing detection methods.

DeferBad enables easy cancellation and reactivation of backdoors.

Experiments show high success and stealthiness across models and datasets.

Abstract

Deep learning models are vulnerable to backdoor attacks, where adversaries inject malicious functionality during training that activates on trigger inputs at inference time. Extensive research has focused on developing stealthy backdoor attacks to evade detection and defense mechanisms. However, these approaches still have limitations that leave the door open for detection and mitigation due to their inherent design to cause malicious behavior in the presence of a trigger. To address this limitation, we introduce Deferred Activated Backdoor Functionality (DABF), a new paradigm in backdoor attacks. Unlike conventional attacks, DABF initially conceals its backdoor, producing benign outputs even when triggered. This stealthy behavior allows DABF to bypass multiple detection and defense methods, remaining undetected during initial inspections. The backdoor functionality is strategically activated only after the model undergoes subsequent updates, such as retraining on benign data. DABF attacks exploit the common practice in the life cycle of machine learning models to perform model updates and fine-tuning after initial deployment. To implement DABF attacks, we approach the problem by making the unlearning of the backdoor fragile, allowing it to be easily cancelled and subsequently reactivate the backdoor functionality. To achieve this, we propose a novel two-stage training scheme, called DeferBad. Our extensive experiments across various fine-tuning scenarios, backdoor attack types, datasets, and model architectures demonstrate the effectiveness and stealthiness of DeferBad.