Fault injection attack on deep neural network
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
Deep neural network (DNN), being able to effectively learn from a training set and provide highly accurate classification results, has become the de-facto technique used in many mission-critical systems. The security of DNN itself is therefore of great concern. In this paper, we investigate the impact of fault injection attacks on DNN, wherein attackers try to misclassify a specified input pattern into an adversarial class by modifying the parameters used in DNN via fault injection. We propose two kinds of fault injection attacks to achieve this objective. Without considering stealthiness of the attack, single bias attack (SBA) only requires to modify one parameter in DNN for misclassification, based on the observation that the outputs of DNN may linearly depend on some parameters. Gradient descent attack (GDA) takes stealthiness into consideration. By controlling the amount of modification to DNN parameters, GDA is able to minimize the fault injection impact on input patterns other than the specified one. Experimental results demonstrate the effectiveness and efficiency of the proposed 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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