Proactive Disentangled Modeling of Trigger-Object Pairings for Backdoor Defense

• URL: http://arxiv.org/abs/2508.01932v1
• Date: Sun, 03 Aug 2025 21:58:15 GMT
• Title: Proactive Disentangled Modeling of Trigger-Object Pairings for Backdoor Defense
• Authors: Kyle Stein, Andrew A. Mahyari, Guillermo Francia III, Eman El-Sheikh,
• Abstract summary: Deep neural networks (DNNs) and generative AI (GenAI) are increasingly vulnerable to backdoor attacks.<n>In this paper, we introduce DBOM, a proactive framework that leverages structured disentanglement to identify and neutralize both seen and unseen backdoor threats.<n>We show that DBOM robustly detects poisoned images prior to downstream training, significantly enhancing the security of training pipelines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SRD: Reinforcement-Learned Semantic Perturbation for Backdoor Defense in VLMs [57.880467106470775]
  Attackers can inject imperceptible perturbations into the training data, causing the model to generate malicious, attacker-controlled captions.<n>We propose Semantic Reward Defense (SRD), a reinforcement learning framework that mitigates backdoor behavior without prior knowledge of triggers.<n>SRD uses a Deep Q-Network to learn policies for applying discrete perturbations to sensitive image regions, aiming to disrupt the activation of malicious pathways.
  arXiv  Detail & Related papers  (2025-06-05T08:22:24Z)
• Robust Anti-Backdoor Instruction Tuning in LVLMs [53.766434746801366]
  We introduce a lightweight, certified-agnostic defense framework for large visual language models (LVLMs)<n>Our framework finetunes only adapter modules and text embedding layers under instruction tuning.<n>Experiments against seven attacks on Flickr30k and MSCOCO demonstrate that ours reduces their attack success rate to nearly zero.
  arXiv  Detail & Related papers  (2025-06-04T01:23:35Z)
• Pulling Back the Curtain: Unsupervised Adversarial Detection via Contrastive Auxiliary Networks [0.0]
  We propose an Unsupervised adversarial detection via Auxiliary Contrastive Networks (U-CAN) to uncover adversarial behavior within auxiliary feature representations.<n>Our method surpasses existing unsupervised adversarial detection techniques, achieving superior F1 scores against four distinct attack methods.
  arXiv  Detail & Related papers  (2025-02-13T09:40:26Z)
• Twin Trigger Generative Networks for Backdoor Attacks against Object Detection [14.578800906364414]
  Object detectors, which are widely used in real-world applications, are vulnerable to backdoor attacks. Most research on backdoor attacks has focused on image classification, with limited investigation into object detection. We propose novel twin trigger generative networks to generate invisible triggers for implanting backdoors into models during training, and visible triggers for steady activation during inference.
  arXiv  Detail & Related papers  (2024-11-23T03:46:45Z)
• CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization [7.282200564983221]
  Large Language Models (LLMs) are vulnerable to backdoor attacks that manipulate outputs via hidden triggers.<n>We propose Internal Consistency Regularization (CROW), a defense leveraging the observation that backdoored models exhibit unstable layer-wise hidden representations when triggered.<n>CROW enforces consistency across layers via adversarial perturbations and regularization during finetuning, neutralizing backdoors without requiring clean reference models or trigger knowledge--only a small clean dataset.
  arXiv  Detail & Related papers  (2024-11-18T07:52:12Z)
• Self-Supervised Representation Learning for Adversarial Attack Detection [6.528181610035978]
  Supervised learning-based adversarial attack detection methods rely on a large number of labeled data. We propose a self-supervised representation learning framework for the adversarial attack detection task to address this drawback.
  arXiv  Detail & Related papers  (2024-07-05T09:37:16Z)
• VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models [65.23688155159398]
  Autoregressive Visual Language Models (VLMs) showcase impressive few-shot learning capabilities in a multimodal context. Recently, multimodal instruction tuning has been proposed to further enhance instruction-following abilities. Adversaries can implant a backdoor by injecting poisoned samples with triggers embedded in instructions or images. We propose a multimodal instruction backdoor attack, namely VL-Trojan.
  arXiv  Detail & Related papers  (2024-02-21T14:54:30Z)
• Pre-trained Trojan Attacks for Visual Recognition [106.13792185398863]
  Pre-trained vision models (PVMs) have become a dominant component due to their exceptional performance when fine-tuned for downstream tasks. We propose the Pre-trained Trojan attack, which embeds backdoors into a PVM, enabling attacks across various downstream vision tasks. We highlight the challenges posed by cross-task activation and shortcut connections in successful backdoor attacks.
  arXiv  Detail & Related papers  (2023-12-23T05:51:40Z)
• Versatile Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers [38.67988745745853]
  We propose a novel trigger called the textbfVisible, textbfSemantic, textbfSample-language, and textbfCompatible (VSSC) trigger. VSSC trigger achieves effective, stealthy and robust simultaneously, which can also be effectively deployed in the physical scenario using corresponding objects.
  arXiv  Detail & Related papers  (2023-06-01T15:42:06Z)
• Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes [51.65308857232767]
  Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Recent research has shown that checking the intrinsic consistencies in the input data is a promising way to detect adversarial attacks. We develop a novel approach to perform context consistency checks using language models.
  arXiv  Detail & Related papers  (2021-08-19T00:52:10Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.