Self-Checking Deep Neural Networks for Anomalies and Adversaries in Deployment
Bibliographic record
Abstract
Deep Neural Networks (DNNs) have been widely adopted, yet DNN models are surprisingly unreliable, which raises significant concerns about their use in critical domains. In this work, we propose that runtime DNN mistakes can be quickly detected and properly dealt within deployment, especially in settings like self-driving vehicles. Just as software engineering (SE) community has developed effective mechanisms and techniques to monitor and check programmed components, our previous work, SelfChecker, is designed to monitor and correct DNN predictions given unintended abnormal test data. SelfChecker triggers an alarm if the decisions given by the internal layer features of the model are inconsistent with the final prediction and providesadvicein the form of an alternative prediction. In this paper, we extend SelfChecker to the security domain. Specifically, we describe SelfChecker++, which we designed to target bothunintendedabnormal test data andintendedadversarial samples. Technically, we develop a technique which can transform any runtime inputs triggering alarms into semantically equivalent inputs, then we feed those transformed inputs to the model. Such runtime transformation nullifies any intended crafted samples, making the model immune to adversarial attacks that craft adversarial samples. We evaluated the alarm accuracy of SelfChecker++ on three DNN models and four popular image datasets, and found that SelfChecker++ triggers correct alarms on 63.10% of wrong DNN predictions, and triggers false alarms on 5.77% of correct DNN predictions. We also evaluated the effectiveness of SelfChecker++ in detecting adversarial examples and found it detects on average 70.09% of such samples with advice accuracy that is 20.89% higher than the original DNN model and 18.37% higher than SelfChecker.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".