Adversarial Attacks Against Shared Knowledge Interpretation in Semantic Communications
Why this work is in the frame
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Bibliographic record
Abstract
Semantic communications (SEMCOM) is a novel communication model that exploits neural networks or deep learning techniques to convey the semantics of the data and contextual reasoning, instead of transmitting full raw bits as in the conventional transmission models. SEMCOM is anticipated to significantly increase the effectiveness of cognitive communications beyond the Shannon theory limit, especially in multimedia services. The transmission efficiency will largely rely on the semantic encoding and decoding process with knowledge storage references at the receiver and the transmitter. However, these processes are highly susceptible to adversarial attacks, given the nature of shared background knowledge without encryption and the vulnerabilities of neural network models. This paper presents two novel targeted and non-targeted adversarial attacks against SEMCOM, e.g., channel inversion attack and naive attack. The attacks are designed to cause maximum disruption to the signals during decoding, aiming to alter the semantic interpretation of recognition models at the receiver. The experimental results indicate that attacks can significantly degrade the perceptual evaluation of speech quality and increase data errors, with semantic decoding performance suffering reductions of up to 2.9 times and 2.3 times, respectively. This degradation can cause misrepresentation of semantic contents. Besides, targeted attacks have a greater impact on speech semantic quality in complex communication circumstances compared to non-targeted attacks. We also suggest two potential defense methods against these physical layer attacks. Accordingly, enhancing adversarial training and removing residual values in the loss function are straightforward solutions to improve the resilience of SEMCOM-based systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it