cf-TDFM: A Framework for Limiting Fault Infusion Attacks on Deep Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Many safety-critical applications have adopted machine learning models like autonomous driving, aviation control, and medical diagnosis. A large number of supervised learning techniques depend on the quality of data. Training data faults make it difficult for models to make correct predictions and may lead to complete failure. It is nearly impossible to scan large data manually to verify its correctness. In this paper, we develop a novel approach to mitigate training data faults by analyzing the data mislabeling using a clustering-based filtering process using features correlation. We evaluate the performance of the proposed approach on various percentages of data faults and observe the accuracy and resilience of the model. Since the performance of the proposed technique does not vary with the percentage of faults in the original data, it has shown a lower value of accuracy delta than state-of-the-art techniques. Thus, it limits effective training of data faults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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