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  1. Home
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  3. On the Vulnerability of Deep Automatic Modulation Classifier
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On the Vulnerability of Deep Automatic Modulation Classifiers to Explainable Backdoor Threats

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Viability
0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: On the Vulnerability of Deep Automatic Modulation Classifiers to Explainable Backdoor Threats

PDF: https://arxiv.org/pdf/2603.25310v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

On the Vulnerability of Deep Automatic Modulation Classifiers to Explainable Backdoor Threats

Overall score: 3/10
Lineage: 7fd9c190bb81…
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Canonical Paper Receipt

Last verification: 2026-04-02T02:30:40.136Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

Missingness
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Dimensions overall score 3.0

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Prior Work
Backdoor Sentinel: Detecting and Detoxifying Backdoors in Diffusion Models via Temporal Noise Consistency
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Physical Backdoor Attack Against Deep Learning-Based Modulation Classification
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Delayed Backdoor Attacks: Exploring the Temporal Dimension as a New Attack Surface in Pre-Trained Models
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Higher Viability
Identifying Adversary Characteristics from an Observed Attack
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Higher Viability
Machine Learning for Network Attacks Classification and Statistical Evaluation of Machine Learning for Network Attacks Classification and Adversarial Learning Methodologies for Synthetic Data Generation
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