Improving Adversarial Robustness of Conjugate Neural Networks with Guided Diversity
Bibliographic record
Abstract
This paper introduces Conjugate Neural Networks (CoNNs), a novel architecture designed to enhance robustness against adversarial attacks. Adversarial attacks pose significant security challenges to neural networks by manipulating input data to cause incorrect model predictions. The proposed CoNNs architecture leverages a dual-model framework where two complementary neural networks are trained in tandem, each using adversarial samples generated by the other, to cancel out adversarial effects and improve overall resilience. We evaluate CoNNs using various deep learning models, including MLP, ResNet, LSTM, and Agricultural-Informed Neural Networks (AINN), across multiple datasets such as Fashion-MNIST, CIFAR10, the data generated by Lorenz system, and agricultural N 2 O emissions data. The results demonstrate that CoNNs significantly outperform traditional single neural network in resisting both Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) attacks. CoNNs maintain higher accuracy and exhibit greater stability under adversarial conditions, making them a promising approach for improving the robustness of neural network-based systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".