A Survey of Secure Semantic Communications

Bizhu Wang; Chen Dong; Dayu Fan; Haixiao Gao; Mengying Sun; Ping Zhang; Rui Meng; Shujun Han; Song Gao; Suyu Lv

REVIEW 4 major objections 5 minor 297 references

A Survey of Secure Semantic Communications

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This survey maps secure semantic communication onto three life-cycle phases and matches threats to defense families.

desk verdict A genuinely useful survey of secure SemCom, but the threat taxonomy needs a fix before it is a reliable reference. read the letter →

arxiv 2501.00842 v2 pith:V7QRNSUE submitted 2025-01-01 cs.CR eess.IVeess.SP

Rui Meng , Song Gao , Dayu Fan , Haixiao Gao , Yining Wang , Xiaodong Xu , Bizhu Wang , Suyu Lv

show 6 more authors

Zhidi Zhang Mengying Sun Shujun Han Chen Dong Xiaofeng Tao Ping Zhang

This is my paper · ORCID

classification cs.CReess.IVeess.SP

keywords Semanticcommunicationwirelesssecurityprivacy6Gfederatedlearningphysical-layerthreattaxonomybackdoorattacks

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Semantic communication (SemCom) replaces bit-perfect transmission with the transfer of meaning: transmitters extract semantic features, receivers reconstruct intent, and both sides rely on AI models as a shared knowledge base. This survey tries to establish that the security and privacy problems of that new setting are not a random list but a structured map, organized by the life cycle of the system: model training, model transfer, and semantic information transmission. For each stage it names the threats—poisoning, gradient leakage, server compromise, bottleneck failures, model-slice attacks, semantic adversarial, eavesdropping, inference, and jamming attacks—and then matches them to nine families of defenses, including robust learning, differential privacy, cryptography, blockchain, model compression, and physical-layer security. A reader who accepts the map can locate where an attack enters a SemCom system and which defense family should meet it, and can see which phases still lack mature protections.

What carries the argument

The organizing device is the SemCom life cycle, defined as the sequence from federated model training on cloud and edge servers, through model and model-slice transfer between nodes, to online semantic information transmission between a transmitter and receiver. Around this life cycle the paper wraps a two-sided taxonomy: a threat table keyed to confidentiality, integrity, and availability, and a defense table that groups countermeasures into nine families. The taxonomy does the argument's work by forcing every attack into a phase and every defense into a category, which is what lets the survey claim completeness and lets readers see which cells of the map are empty or underdeveloped.

What would settle it

Open the two papers cited for the bottleneck category (markers [85] and [98] in the survey) and check whether either describes an adversary that deliberately exhausts bandwidth or causes dropout; if both describe only capacity limits and resource contention, then that row of the threat table is not an attack, and the survey's adversarial threat taxonomy is contradicted by its own evidence.

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Extended reading notes

Core claim

The paper's central claim is that securing SemCom can be understood phase by phase because each phase has its own assets and exposure. During model training, the asset is the knowledge base and the risks are poisoning, gradient leakage, server compromise, and communication bottlenecks in federated updates. During model transfer, the asset is the model or its slices, and the risks are availability loss and forgery. During semantic information transmission, the asset is the meaning carried by semantic symbols, and the risks are adversarial perturbation, eavesdropping, inference, and jamming. The paper then claims that existing defenses—from data cleaning and robust aggregation to homomorphic encryption, blockchain, and physical-layer security—can be classified to cover exactly these threats, and it identifies open problems where coverage is still thin. The contribution is not a new protocol but a taxonomy that turns a scattered literature into a decision aid.

Load-bearing premise

The survey's claim depends on its threat taxonomy being genuinely a list of adversarial acts; at least one named category, 'attacks against communication bottlenecks,' describes bandwidth limits and edge-server dropout that can occur without any adversary, so if that category is not a real attack class the map mixes resource constraints with malicious actions.

Editorial extensions

If this is right

A researcher who encounters a new attack on a SemCom system can first locate it in one of the three life-cycle phases and then narrow the defense search to the corresponding family in the nine-category table.
Defenses developed for general federated learning and AI security—robust aggregation, data cleaning, differential privacy, backdoor detection—transfer to the training and transfer phases of SemCom rather than applying only to the wireless transmission stage.
Cryptography and blockchain are positioned as protection for model updates and model slices, while physical-layer security is positioned for semantic eavesdropping, inference, and jamming, giving each defense family a clear home.
The survey's open-problem list—dynamic data cleaning, explainable robust learning, differential-privacy-based deep JSCC, efficient homomorphic encrypted SemCom, smart-contract-enabled SemCom, and semantic channel fingerprint databases—defines a concrete near-term research agenda for the field.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension is to split the 'bottleneck' category into adversarial denial-of-service versus non-malicious resource constraints, since the two need different defenses and the survey currently treats them together.
The phase-by-phase map suggests that semantic fidelity—how well meaning survives an attack—could serve as a unified evaluation metric across all nine defense families, a metric the survey itself does not apply.
If the taxonomy is right, then securing the model transfer phase deserves as much attention as securing the wireless channel, because model-slice forgery and availability attacks can corrupt semantic communication before any symbol is sent.
The survey borrows heavily from federated-learning security; a natural next step it leaves implicit is to benchmark which transferred defenses preserve reconstruction quality under realistic channel noise, not just classification accuracy.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is a genuinely useful survey of secure semantic communications, and the lifecycle organization (training, transfer, transmission) is a real contribution over the earlier surveys it compares against. The tables are dense but navigable; a newcomer could use this to get oriented quickly, and the defense taxonomy covers the expected ground with relevant citations.

The main soft spot is exactly what the stress-test flags. Section 3.3.4 and Table 3 call "attacks against communication bottlenecks" an availability threat in the training stage, but the text describes communication overhead, limited bandwidth, delays, and edge-server dropout—all things that happen without any adversary. The paper itself acknowledges in §3.2.3 that availability issues include non-malicious hardware failures and software downtime. Labeling resource constraints as attacks conflates engineering limits with adversarial action, and it leaks into Table 4, where model compression is mapped as defense against this non-attack. A threat-modeling reader could easily over-allocate defenses toward bandwidth problems. The fix is straightforward: either drop "attacks" and call the category "availability risks," or separate intentional denial from resource constraints.

Other issues are smaller. Equations (2) and (3) are not meaningful as written—Adv[A,E] = |Pr(M0 - M1)| is not a standard advantage definition, and the notation is never explained. Equation (1) is also vague. There are a few typos ("F or exmaple", "cloude server") that an editor should catch. I don't see the self-citations as a problem; that's normal in a survey, and the cited works are relevant.

The central value holds up. The lifecycle-to-threat mapping is genuinely new, and the synthesis is broad. This deserves a serious referee pass, but it should be conditional on fixing the taxonomy and cleaning up the equations.

Who this is for: graduate students and engineers entering SemCom security, and practitioners who want a map of defenses. I'd bring it to a reading group and would cite it once the taxonomy is corrected.

Recommendation: send to peer review; require the taxonomy fix before acceptance.

Referee Report

4 major / 5 minor

Summary. This survey aims to provide a comprehensive, structured overview of security and privacy in semantic communication (SemCom). It organizes the field around a three-phase life cycle—model training, model transfer, and semantic information transmission—and, for each phase, lists representative threats and maps them to defensive technologies such as data cleaning, robust learning, backdoor defenses, adversarial training, differential privacy, cryptography, blockchain, model compression, and physical-layer security. The paper also includes architectural background, tables of threats and defenses, and a set of future research directions.

Significance. If the taxonomy were reliable, the survey would be a useful entry point for researchers because of its broad citation coverage, its life-cycle organization, and its tabular summaries of both attacks and defenses. The tables provide a quick reference that is genuinely convenient. However, the paper's central contribution is the threat taxonomy and its defense mapping, and part of that taxonomy conflates non-adversarial resource constraints with intentional attacks. Because the reliability of the entire structured map depends on that taxonomy, the survey's practical value for threat modeling is currently limited. The paper does not contain machine-checked proofs or reproducible experiments, but as a survey its value is organizational, and that value is real once the categorization errors are corrected.

major comments (4)

[§3.3.4 and Table 3] The category "attacks against communication bottlenecks" does not describe an attack. The phenomena listed in §3.3.4—high communication overhead, limited bandwidth, delays causing edge-server dropout, and data heterogeneity—are resource constraints and reliability issues that can occur with no adversary present. The paper itself acknowledges in §3.2.3 that typical availability issues include non-malicious hardware failures and software downtime. Yet Table 3 lists "attacks against communication bottlenecks" as a training-stage threat with property "Availability," and Table 4 pairs model compression with this category as a defensive measure. This conflates non-adversarial engineering limits with malicious security threats and undermines the reliability of the life-cycle-to-threat mapping. Please either reframe this entry as a non-adversarial availability challenge, or introduce an explicit adversary model that makes it an attack, and adjust Tables 3 and 4 accordingly.
[§3.4.1 and Table 3] The "model slice availability attacks" category mixes at least three distinct issues: confidentiality failures caused by insufficient isolation, fairness problems caused by uneven resource allocation, and Denial-of-Service attacks. The table row states that such attacks "corrupt the parameters of the model slices," which is an integrity effect rather than an availability effect. Because the same category is assigned the single property "Availability," the CIA labeling is internally inconsistent. Please decompose these into separate threat entries with consistent security properties, or rewrite the category so that it describes one coherent attack type.
[§3.3.2, Eq. (1)] Equation (1) is not a derivation and is notationally ill-defined. The quantities ∇W and ∇W′ are not defined, the function f(x′,s′,y′) is not defined, and the constraint "x′,s′,y′ ∼ f(x′,s′,y′)" is circular unless f is a sampling distribution, which is not what the surrounding text states. Earlier in the same subsection, x′ is used for both the original information and the semantic information, compounding the ambiguity. Because this equation is offered as the basis for the claim that SemCom gradient leakage is "deeper and easier," it needs either a precise formulation with all variables defined or an explicit downgrade to an informal illustrative statement.
[§3.5, Eqs. (2)–(3)] The advantage function Adv[A,E] = |Pr(M0 − M1)| is not a well-formed probability expression: M0 and M1 are defined as events, so the expression should be a difference of probabilities (e.g., |Pr[M0] − Pr[M1]|), typically expressed as a guessing advantage relative to 1/2. Equation (3) adds "model" as an extra argument but never defines the distribution over models or the associated advantage function. As written, these equations do not state the semantic-security claim that the text attributes to them. Please replace them with a standard semantic-security definition and then state, as a separate formal assumption, how the attacker's possession of a reconstructed model affects the advantage.

minor comments (5)

[§4.5, Eq. (4)] The (ε,δ)-differential privacy inequality is misstated: it should read Pr[M(d_C) ∈ S_C] ≤ e^ε · Pr[M(d′_C) ∈ S_C] + δ, not Pr[e^ε M(d′_C) ∈ S_C] + δ.
[§3.3.1, §3.3.4, Table 3] There are several typos and grammatical errors: "Backdoor attack is a special kind of backdoor attacks," "the cloude server," "exmaple," and "V ulnerability" in Table 3. A careful proofreading pass is needed.
[Table 2] The acronym table appears to have a formatting problem in the DNN/PHE row, where entries seem to be merged. Please check the table layout.
[§4.1, §4.2, §4.3] Repeated phrases such as "The below, as shown in Table 5, the following content" are ungrammatical and should be revised to plain introductory sentences.
[§3.5.2] The sentence "privacy properties of SemCom degrade much more grace" contains a typo (probably "gracefully") and should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a taxonomic survey with no derivational claims that reduce to their inputs.

full rationale

This is a survey paper rather than a derivation, so the circularity patterns involving fitted inputs, self-referential equations, and imported uniqueness theorems do not apply. The paper's contribution is a lifecycle-based organization of existing threats and defenses in secure semantic communication. Its equations (Eq. 1 for gradient approximation, Eqs. 2-3 for semantic advantage, Eq. 4 for differential privacy) restate standard definitions from cited prior work and are not used to generate predictions from fitted parameters. The taxonomy does contain a questionable classification: §3.3.4 labels high communication overhead, limited bandwidth, and edge-server dropout as 'attacks against communication bottlenecks,' even though the described phenomena can be non-adversarial resource constraints; however, this is a correctness and scope concern about the threat model, not a circular reduction. Similarly, the paper's reliance on works plausibly from its own group (e.g., [183] on homomorphic-encrypted deep JSCC) is one example within a broad survey of defense technologies, and accepting or rejecting that work does not force the survey's organizational claims. No load-bearing step is equivalent by construction to its input, so the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

No free parameters or invented entities are introduced because the paper is a literature review. The primary unstated premise is that the surveyed literature is representative and correctly summarized.

assumptions (1)

domain assumption The cited works are accurately represented and their conclusions are correct.
The survey's statements about threats and defenses rely on the validity of the referenced papers. If several key references are mischaracterized, the survey's conclusions could be wrong.

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Cite this review

Pith. "Pith review of A Survey of Secure Semantic Communications." pith.science (2026). https://pith.science/paper/V7QRNSUE

@misc{pith2026250100842,
  author       = {Pith},
  title        = {Pith review of: A Survey of Secure Semantic Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V7QRNSUE}},
  note         = {Machine review of arXiv:2501.00842}
}

read the original abstract

Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of ``Shannon's trap" by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields.

Figures

Figures reproduced from arXiv: 2501.00842 by the authors.

**Figure 1.** Organizations of this paper. 3 provides the security and privacy violation issues during each stage of SemCom. Sec. 4 provides insights into existing defense mechanisms to enhance the security and privacy of SemCom. Sec. 5 looks forward to the future research direction. Finally, Sec. 6 concludes this paper. The acronyms used in this paper are listed in Tab. 2 [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗

**Figure 2.** Comparison of the link-level architecture between traditional communication [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** Network-level architecture of SemCom systems, where the semantic collabora [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

Figures from the paper (24 more)

**Figure 4.** Figure 4: Life cycle of SemComs, where a centralized model is first trained on the cloud [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]

**Figure 5.** Figure 5: Data cleaning in DL [117], where the process ensures the quality and consistency [PITH_FULL_IMAGE:figures/full_fig_p031_5.png]

**Figure 6.** Figure 6: Diagram of FL [125], where data is processed in a decentralized manner by [PITH_FULL_IMAGE:figures/full_fig_p037_6.png]

**Figure 7.** Figure 7: Malicious client attacks in FL [129], where malicious clients attempt to leak [PITH_FULL_IMAGE:figures/full_fig_p039_7.png]

**Figure 8.** Figure 8: Comparison of noise distributions under the expectation-based model and the [PITH_FULL_IMAGE:figures/full_fig_p041_8.png]

**Figure 9.** Figure 9: Classification of neural network backdoor defense [139], where the process of [PITH_FULL_IMAGE:figures/full_fig_p044_9.png]

**Figure 10.** Figure 10: The workflow of meta-classifier defense [139], where the data that reflect the [PITH_FULL_IMAGE:figures/full_fig_p049_10.png]

**Figure 11.** Figure 11: Process of adversarial training [150], where adversarial training enhances tradi [PITH_FULL_IMAGE:figures/full_fig_p051_11.png]

**Figure 12.** Figure 12: Semantic distance minimization mechanism [152], where lines of the same [PITH_FULL_IMAGE:figures/full_fig_p055_12.png]

**Figure 13.** Figure 13: Differential privacy mechanism [162], where depicts a privacy-preserving system [PITH_FULL_IMAGE:figures/full_fig_p061_13.png]

**Figure 14.** Figure 14: The three phase of differential privacy deployed in DL model [163], where [PITH_FULL_IMAGE:figures/full_fig_p062_14.png]

**Figure 15.** Figure 15: The architecture of a Paillier federated network [181], where each client requests [PITH_FULL_IMAGE:figures/full_fig_p073_15.png]

**Figure 16.** Figure 16: The diagram of SMPC [194], where multiple parties collaboratively com [PITH_FULL_IMAGE:figures/full_fig_p075_16.png]

**Figure 17.** Figure 17: SGX Diagram [186], where secret shares and random masks are combined with [PITH_FULL_IMAGE:figures/full_fig_p079_17.png]

**Figure 18.** Figure 18: Schematic diagram of the algorithm based on the FFT [187], where secret shares [PITH_FULL_IMAGE:figures/full_fig_p081_18.png]

**Figure 19.** Figure 19: Functional diagram of a blockchain network [217], where blockchain transaction [PITH_FULL_IMAGE:figures/full_fig_p085_19.png]

**Figure 20.** Figure 20: An illustrative framework of Blockchain-Semantic ecosystems [218], where be [PITH_FULL_IMAGE:figures/full_fig_p089_20.png]

**Figure 21.** Figure 21: Combination of model pruning and FL [231], where two stages are involved, [PITH_FULL_IMAGE:figures/full_fig_p097_21.png]

**Figure 22.** Figure 22: Comparison between quantization aware training and post training quanti [PITH_FULL_IMAGE:figures/full_fig_p099_22.png]

**Figure 23.** Figure 23: Residual error based knowledge distillation [238], where the Assistant (middle) [PITH_FULL_IMAGE:figures/full_fig_p105_23.png]

**Figure 24.** Figure 24: Semantic-bit coexisting communication framework [263], where a single-cell [PITH_FULL_IMAGE:figures/full_fig_p111_24.png]

**Figure 25.** Figure 25: Semantic-forward for cooperative communications [286], where SemComs are [PITH_FULL_IMAGE:figures/full_fig_p116_25.png]

**Figure 26.** Figure 26: Unified framework for IRS-aided SemCom systems [274], where the edge device [PITH_FULL_IMAGE:figures/full_fig_p117_26.png]

**Figure 27.** Figure 27: Environment semantics enabled PLA [279], where angle-delay features are ex [PITH_FULL_IMAGE:figures/full_fig_p120_27.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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