Detecting Backdoor Attacks via Similarity in Semantic Communication Systems
Semantic communication systems, which leverage Generative AI (GAI) to transmit semantic meaning rather than raw data, are poised to revolutionize modern communications. However, they are vulnerable to backdoor attacks, a type of poisoning manipulation that embeds malicious triggers into training datasets. As a result, Backdoor attacks mislead the inference for poisoned samples while clean samples remain unaffected. The existing defenses may alter the model structure (such as neuron pruning that potentially degrades inference performance on clean inputs, or impose strict requirements on data formats (such as ``Semantic Shield" that requires image-text pairs). To address these limitations, this work proposes a defense mechanism that leverages semantic similarity to detect backdoor attacks without modifying the model structure or imposing data format constraints. By analyzing deviations in semantic feature space and establishing a threshold-based detection framework, the proposed approach effectively identifies poisoned samples. The experimental results demonstrate high detection accuracy and recall across varying poisoning ratios, underlining the significant effectiveness of our proposed solution.
Code (0)
등록된 구현이 없습니다.
Tasks
Semantic CommunicationSemantic SimilaritySemantic Textual SimilarityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
BlackMirror: Black-Box Backdoor Detection for Text-to-Image Models via Instruction-Response Deviation
This paper investigates the challenging task of detecting backdoored text-to-image models under black-box settings and introduces a novel detection framework BlackMirror. Existing approaches typically rely on analyzing i…
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks
This paper highlights vulnerabilities of deep learning-driven semantic communications to backdoor (Trojan) attacks. Semantic communications aims to convey a desired meaning while transferring information from a transmitt…
Backdoor AttackDecoderDeep LearningA Channel-Triggered Backdoor Attack on Wireless Semantic Image Reconstruction
This paper investigates backdoor attacks in image-oriented semantic communications. The threat of backdoor attacks on symbol reconstruction in semantic communication (SemCom) systems has received limited attention. Previ…
Backdoor AttackImage ReconstructionSemantic CommunicationShortcuts Everywhere and Nowhere: Exploring Multi-Trigger Backdoor Attacks
Backdoor attacks have become a significant threat to the pre-training and deployment of deep neural networks (DNNs). Although numerous methods for detecting and mitigating backdoor attacks have been proposed, most rely o…
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input are able to mislead the models to give wrong results. Although defenses against adv…
Adversarial DefenseBackdoor Attack