Backdoor Attacks on Semantic Symbol Reconstruction in Semantic Communication Systems and Effective Defense Strategies

Core Concepts

Semantic communication systems powered by deep learning are vulnerable to backdoor attacks that can manipulate the semantics of reconstructed symbols. This paper introduces a new backdoor attack paradigm targeting semantic symbols (BASS) and proposes effective defense strategies to mitigate such attacks.

Abstract

The paper presents a novel backdoor attack paradigm targeting semantic communication systems, where the adversary aims to manipulate the semantics of the reconstructed symbols at the receiver. Unlike traditional backdoor attacks that focus on classification outcomes, BASS targets the high-dimensional semantic outputs in semantic communication, such as text, speech, and images.
The key highlights are:

System Model and Threat Model:

A semantic communication system with one transmitter and one receiver is considered.
The adversary can manipulate the training datasets at both the transmitter and the receiver to introduce triggers and modify the semantic symbols and labels.

Backdoor Attack Paradigm (BASS):

During training, the adversary introduces triggers into a subset of the input samples at the transmitter and modifies the corresponding semantic symbols and labels at the receiver.
During inference, the adversary injects triggers to poison the inputs, resulting in the produced reconstructed symbols being the target symbols specified by the adversary.

Defense Mechanisms:

Data Location-based Defense: A training framework is proposed to prevent data alteration by pushing both the transmitter's and receiver's datasets to the transmitter.
Reverse Engineering-based Defense: An optimization problem is formulated to estimate the trigger pattern by minimizing the semantic feature distance between poisoned samples.
Pruning-based Defense: A post-training pruning algorithm is designed to eliminate the backdoor by pruning the neurons in the encoder of the semantic communication network.

Simulation Results:

The effectiveness of the proposed attack and defense methods is evaluated under different parameter settings, including varying signal-to-noise ratios (SNRs) and poison ratios.
The results demonstrate the effectiveness of the BASS attack and the proposed defense strategies in mitigating the backdoor without significant performance degradation.

Stats

The paper provides the following key statistics and figures:

The average peak signal-to-noise ratio (PSNR) of clean data (PSNRC) and poisoned data (PSNRP) for different poison ratios and compression ratios.
Comparison of PSNR for clean and poisoned models across different SNR levels and compression ratios.
Trigger pattern estimated by the proposed reverse engineering-based defense method.
Reconstruction accuracy of clean data and poisoned data versus pruning ratio.
F1-score comparison for models trained with MNIST and CIFAR10 across different poison ratios.
Difference (dc) between normalized pruning ratio and normalized reconstruction accuracy versus pruning ratio.

Quotes

None.

Key Insights Distilled From

Backdoor Attacks and Defenses on Semantic-Symbol Reconstruction in Semantic Communications

by Yuan Zhou,Ro... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13279.pdf

Deeper Inquiries

How can the proposed defense strategies be extended to handle backdoor attacks in other types of deep learning-based communication systems, such as wireless signal classification or end-to-end communication

The defense strategies proposed in the context of backdoor attacks on semantic communication systems can be extended to handle similar threats in other types of deep learning-based communication systems. For instance, in wireless signal classification systems, where the integrity of signal classification is crucial, similar defense mechanisms can be applied. By implementing the training framework designed to prevent backdoor attacks, utilizing reverse engineering techniques for trigger estimation, and employing pruning-based algorithms to eliminate backdoors, these systems can enhance their resilience against malicious attacks. Additionally, in end-to-end communication systems, the same principles can be adapted to safeguard the integrity of data transmission and reception. By incorporating these defense strategies, deep learning-based communication systems can fortify their security posture and mitigate the risks associated with backdoor attacks.

What are the potential implications of backdoor attacks on the trustworthiness and reliability of semantic communication systems in critical applications, such as healthcare or autonomous systems

Backdoor attacks on semantic communication systems can have significant implications for the trustworthiness and reliability of these systems, especially in critical applications such as healthcare or autonomous systems. In healthcare, where the accuracy and confidentiality of medical data are paramount, a compromised semantic communication system could lead to misinterpretation of critical information or unauthorized access to sensitive patient data. This could result in misdiagnosis, incorrect treatment plans, or privacy breaches, jeopardizing patient safety and confidentiality. Similarly, in autonomous systems like self-driving cars, backdoor attacks could manipulate the semantic communication between vehicle components, leading to erroneous decision-making and potentially catastrophic accidents. The trustworthiness and reliability of semantic communication systems are essential in critical applications, and safeguarding against backdoor attacks is imperative to ensure the integrity and security of these systems.

What other types of adversarial attacks, beyond backdoor attacks, could pose threats to semantic communication systems, and how can they be effectively mitigated

Beyond backdoor attacks, other types of adversarial attacks could pose threats to semantic communication systems, necessitating effective mitigation strategies. One such threat is adversarial examples, where imperceptible perturbations to input data can lead to misclassification or erroneous semantic interpretation. To mitigate adversarial examples, techniques like adversarial training, input preprocessing, and robust model architectures can be employed to enhance the resilience of semantic communication systems. Moreover, model inversion attacks, where an adversary attempts to infer sensitive information from the output of a semantic communication system, pose a privacy risk. Defenses such as differential privacy, data perturbation, and output sanitization can help mitigate the impact of model inversion attacks and protect the privacy of sensitive information. By proactively addressing a range of adversarial threats beyond backdoor attacks, semantic communication systems can bolster their security posture and maintain the trustworthiness of their operations.