Attack-tolerant Trajectory Prediction using Generative Adversarial Network Secured by Blockchain Application to the UAS-S4 Ehécatl
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2192.vid A robust data-driven algorithm is designed for Aircraft Trajectory Prediction (ATP). A Neural Network model predicts future trajectories of aircraft relying on the input vector containing latitude, longitude, altitude, heading, speed, and time. The model is constructed based on Generative Adversarial Networks (GANs) architecture. The GAN model is highly robust against Adversarial Attacks due to its inherent generative feature. Blockchain is used as a Ledger Technology (LT) in order to trustworthy store the legitimate predicted values that are used for further predictions. In other words, blocks refuse storage of adversarial predicted values as they are detected as adversarial samples and are not approved by the Blockchain. For validation studies, trajectories for training the GAN model were generated using our UAS-S4 Ehécatl simulation model. Adversarial Attack Tolerance based on fooling rates was considered as performance index. The obtained results confirmed the excellent effectiveness of our Blockchain-secured GAN in the case of adversarial white-box and black-box attacks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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