Open Access

ARTICLE

Proactive Disentangled Modeling of Trigger–Object Pairings for Backdoor Defense

Kyle Stein^1,*, Andrew A. Mahyari^1,2, Guillermo Francia III³, Eman El-Sheikh³

1 Department of Intelligent Systems and Robotics, University of West Florida, Pensacola, FL 32514, USA
2 Florida Institute for Human and Machine Cognition (IHMC), Pensacola, FL 32502, USA
3 Center for Cybersecurity, University of West Florida, Pensacola, FL 32502, USA

* Corresponding Author: Kyle Stein. Email:

(This article belongs to the Special Issue: Towards Privacy-preserving, Secure and Trustworthy AI-enabled Systems)

Computers, Materials & Continua 2025, 85(1), 1001-1018. https://doi.org/10.32604/cmc.2025.068201

Received 23 May 2025; Accepted 11 July 2025; Issue published 29 August 2025

Abstract

Deep neural networks (DNNs) and generative AI (GenAI) are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify or misinterpret target labels. Beyond traditional single-trigger scenarios, attackers may inject multiple triggers across various object classes, forming unseen backdoor-object configurations that evade standard detection pipelines. In this paper, we introduce DBOM (Disentangled Backdoor-Object Modeling), a proactive framework that leverages structured disentanglement to identify and neutralize both seen and unseen backdoor threats at the dataset level. Specifically, DBOM factorizes input image representations by modeling triggers and objects as independent primitives in the embedding space through the use of Vision-Language Models (VLMs). By leveraging the frozen, pre-trained encoders of VLMs, our approach decomposes the latent representations into distinct components through a learnable visual prompt repository and prompt prefix tuning, ensuring that the relationships between triggers and objects are explicitly captured. To separate trigger and object representations in the visual prompt repository, we introduce the trigger–object separation and diversity losses that aids in disentangling trigger and object visual features. Next, by aligning image features with feature decomposition and fusion, as well as learned contextual prompt tokens in a shared multimodal space, DBOM enables zero-shot generalization to novel trigger-object pairings that were unseen during training, thereby offering deeper insights into adversarial attack patterns. Experimental results on CIFAR-10 and GTSRB demonstrate that DBOM robustly detects poisoned images prior to downstream training, significantly enhancing the security of DNN training pipelines.

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Keywords

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