Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning

• URL: http://arxiv.org/abs/2604.23231v1
• Date: Sat, 25 Apr 2026 09:53:13 GMT
• Title: Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning
• Abstract summary: SemBugger is a polymorphic SC backdoor with finely-grained controls over the SC knowledge to generate diverse malicious results from the system.<n>To augment SC security, we propose a provable robustness defense that resists SemBugger's homogeneous attacks through a controlled noise mechanism.
• Score: 38.125666408386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic Communication (SC) backdoor attacks aim to utilize triggers to manipulate the system into producing predetermined outputs via backdoored shared knowledge. Current SC backdoors adopt monomorphic paradigms with single attack target, which suffers from limited attack diversity, efficiency, and flexibility in heterogeneous downstream scenarios. To overcome the limitations, we propose SemBugger, a polymorphic SC backdoor. By dynamically adjusting the trigger intensity, SemBugger finely-grained controls over the SC knowledge to generate diverse malicious results from the system. Specifically, SemBugger is realized through a multi-effect poisoning-training framework. It introduces graded-intensity triggers to poison training data and optimizes SC systems with hierarchical malicious loss. The trained system's knowledge dynamically adapts to trigger intensity in inputs to yield target outputs, all while preserving transmission fidelity for benign samples. Moreover, to augment SC security, we propose a provable robustness defense that resists SemBugger's homogeneous attacks through a controlled noise mechanism. It operates via strategically adding noise in SC inputs, and we formally provide a theoretical lower bound on the defense efficacy. Experiments across diverse SC models and benchmark datasets indicate that SemBugger attains high attack efficacy while maintaining the regular functionality of SC systems. Meanwhile, the designed defense effectively neutralizes SemBugger attacks.

Related papers

• Fine-grained Distributed Backdoor Attacks in Federated Learning [9.374638533613313]
  Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks.<n>We propose a fine-grained distributed backdoor attack framework (FDBA)<n>This framework uses dynamic trigger generation and embedding vector optimization to perform attacks with fewer poisoned samples.
  arXiv  Detail & Related papers  (2026-09-07T07:46:52Z)
• FAROS: Robust Federated Learning with Adaptive Scaling against Backdoor Attacks [9.466036066320946]
  backdoor attacks pose a significant threat to Federated Learning (FL)<n>We propose FAROS, an enhanced FL framework that incorporates Adaptive Differential Scaling (ADS) and Robust Core-set Computing (RCC)<n>RCC effectively mitigates the risk of single-point failure by computing the centroid of a core set comprising clients with the highest confidence.
  arXiv  Detail & Related papers  (2026-01-05T06:55:35Z)
• Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models [17.839413035304748]
  Backdoor unalignment attacks against Large Language Models (LLMs) enable the stealthy compromise of safety alignment using a hidden trigger.<n>We introduce BEAT, a black-box defense that detects triggered samples during inference to deactivate the backdoored LLM.<n>Our method addresses the challenges of sample-dependent targets from an opposite perspective.
  arXiv  Detail & Related papers  (2025-06-19T16:30:56Z)
• A Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction [22.485161150177866]
  Previous research on backdoor attacks targeting SemCom symbol reconstruction primarily focuses on input-level triggers.<n>We propose a novel channel-triggered backdoor attack (CT-BA) framework that exploits inherent wireless channel characteristics as activation triggers.<n>Our attack achieves near-perfect attack success rate (ASR) while maintaining effective stealth.
  arXiv  Detail & Related papers  (2025-03-31T09:17:10Z)
• Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in CLIP [51.04452017089568]
  Class-wise Backdoor Prompt Tuning (CBPT) is an efficient and effective defense mechanism that operates on text prompts to indirectly purify CLIP.<n>CBPT significantly mitigates backdoor threats while preserving model utility.
  arXiv  Detail & Related papers  (2025-02-26T16:25:15Z)
• ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models [55.93380086403591]
  Generative large language models are vulnerable to backdoor attacks.<n>$textitELBA-Bench$ allows attackers to inject backdoor through parameter efficient fine-tuning.<n>$textitELBA-Bench$ provides over 1300 experiments.
  arXiv  Detail & Related papers  (2025-02-22T12:55:28Z)
• Robust and Transferable Backdoor Attacks Against Deep Image Compression With Selective Frequency Prior [118.92747171905727]
  This paper introduces a novel frequency-based trigger injection model for launching backdoor attacks with multiple triggers on learned image compression models.<n>We design attack objectives tailored to diverse scenarios, including: 1) degrading compression quality in terms of bit-rate and reconstruction accuracy; 2) targeting task-driven measures like face recognition and semantic segmentation.<n>Experiments show that our trigger injection models, combined with minor modifications to encoder parameters, successfully inject multiple backdoors and their triggers into a single compression model.
  arXiv  Detail & Related papers  (2024-12-02T15:58:40Z)
• Towards Stable Backdoor Purification through Feature Shift Tuning [22.529990213795216]
  Deep neural networks (DNN) are vulnerable to backdoor attacks. In this paper, we start with fine-tuning, one of the most common and easy-to-deploy backdoor defenses. We introduce Feature Shift Tuning (FST), a method for tuning-based backdoor purification.
  arXiv  Detail & Related papers  (2023-10-03T08:25:32Z)
• Discriminator-Free Generative Adversarial Attack [87.71852388383242]
  Agenerative-based adversarial attacks can get rid of this limitation. ASymmetric Saliency-based Auto-Encoder (SSAE) generates the perturbations. The adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.
  arXiv  Detail & Related papers  (2021-07-20T01:55:21Z)
• 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.