Maximising robustness and diversity for improving the deep neural network safety
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
Abstract This article proposes a novel yet efficient defence method against adversarial attack(er)s aimed to improve the safety of deep neural networks. Removing the adversarial noise by refining adversarial samples as a defence strategy is widely investigated in previous works. Such methods are simply broken if an attacker has access to both main and refiner networks. To cope with this weakness, the authors propose to refine the input samples relying on a set of encoder–decoders, which are trained in such a way to reconstruct the samples on completely different feature spaces. To this end, the authors learn several encoder–decoder networks and force their latent spaces to have a maximum diversion. In this way, if attacker gets access to one of the refiner networks, other ones can play as a defence network. The evaluation of the proposed method confirms its performance against adversarial samples.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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