Physical Security of Deep Learning on Edge Devices: Comprehensive Evaluation of Fault Injection Attack Vectors
Bibliographic record
Abstract
Abstract Decision making tasks carried out by the usage of deep neural networks are successfully taking over in many areas, including those that are security critical, such as healthcare, transportation, smart grids, where intentional and unintentional failures can be disastrous. Edge computing systems are becoming ubiquitous nowadays, often serving deep learning tasks that do not need to be sent over to servers. Therefore, there is a necessity to evaluate the potential attacks that can target deep learning in the edge. In this work, we present evaluation of deep neural networks (DNNs) reliability against fault injection attacks. We first experimentally evaluate DNNs implemented in an embedded device by using laser fault injection to get the insight on possible attack vectors. We show practical results on four activation functions, ReLu, softmax, sigmoid, and tanh. We then perform a deep study on DNNs based on derived fault models by using several different attack strategies based on random faults. We also investigate a powerful attacker who can find effective fault location based on genetic algorithm, to show the most efficient attacks in terms of misclassification success rates. Finally, we show how a state of the art countermeasure against model extraction attack can be bypassed with a fault attack. Our results can serve as a basis to outline the susceptibility of DNNs to physical attacks which can be considered a viable attack vector whenever a device is deployed in hostile environment.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".