Securing GAI-Driven Semantic Communications: A Novel Defense Against Backdoor Attacks

Semantic communication systems, powered by Generative AI (GAI), are transforming the way information is transmitted by focusing on the meaning of data rather than raw content.

Unlike traditional communication methods, these systems encode semantic features such as text, images, or speech into low-dimensional vectors, significantly reducing bandwidth usage while maintaining the integrity of transmitted information.

This innovation has found applications in data-intensive domains like augmented reality (AR), Internet of Things (IoT), and autonomous systems.

However, the reliance on deep learning models exposes semantic communication systems to backdoor attacks.

These attacks covertly embed malicious triggers into training datasets or models, causing systems to misinterpret poisoned inputs while leaving clean data unaffected.

For example, in autonomous driving scenarios, a backdoor attack could manipulate sensor data to misclassify a stop sign as a yield sign, posing significant safety risks.

The Threat of Backdoor Attacks

Backdoor attacks exploit the training phase by embedding hidden triggers in datasets or models.

These triggers are designed to activate specific malicious behaviors during inference without impacting normal operations on clean data.

Current defenses against such attacks often involve neuron pruning or reverse engineering but come with limitations.

Neuron pruning, for instance, can degrade the model’s performance on clean inputs, while other methods impose strict data format requirements that limit their applicability.

To address these shortcomings, researchers have introduced a novel defense mechanism leveraging semantic similarity analysis.

This approach detects poisoned samples by analyzing deviations in the semantic feature space without altering the model structure or imposing constraints on input formats.

A Novel Defense Framework

The proposed defense mechanism employs a threshold-based detection framework to identify poisoned samples effectively:

1. Baseline Establishment: A clean dataset is used to compute baseline semantic vectors that represent expected patterns in semantic space.
2. Threshold Determination: A similarity metric measures deviations between input samples and the baseline.
3. Sample Classification: Samples exceeding the threshold are flagged as poisoned and excluded from further processing.

This framework ensures high detection accuracy and recall across varying poisoning ratios while preserving the model’s ability to process clean inputs effectively.

Extensive experiments were conducted using datasets like MNIST to evaluate the proposed defense mechanism under different poisoning ratios (5%-50%).

Results demonstrated that the mean-threshold strategy achieved perfect recall (100%) and high accuracy (96%-99%) across scenarios.

According to the report, the max-threshold approach also maintained high accuracy but showed slightly lower recall due to its stricter classification criteria.

Adjusting thresholds dynamically based on percentiles further optimized performance, achieving an ideal balance between recall and accuracy at specific settings.

This innovative defense mechanism represents a significant advancement in securing GAI-driven semantic communication systems against backdoor attacks.

By leveraging semantic similarity analysis, it ensures robust protection without compromising system performance or flexibility.

Future research will focus on extending this framework to handle more complex data types like audio and video while exploring adaptive threshold-setting methods to counter evolving attack strategies.

As semantic communication continues to shape next-generation networks, such advancements will be critical in ensuring their security and reliability.

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