An Adversarial Attack Based on Incremental Learning Techniques for Unmanned in 6G Scenes
Bibliographic record
Abstract
With the development of artificial intelligence(AI), unmanned vehicles can relieve traffic jamming and decrease the risk of traffic accidents, where deep neural networks (DNNs) play an important role and have become one of the most critical technologies. Nevertheless, DNNs are still susceptible to adversarial examples. Even worse, they also show severe performance degradation when the system needs DNNs to learn new knowledge without forgetting the old one. As unmanned vehicles travel on the road, they need to frequently learn new categories and different representations. Learning all data after the new sample arrives will expend a lot of time and space. As a result, it will affect the deployment of artificial intelligence in unmanned scenes. In recent years, it has been observed that incremental learning technology can solve the above challenges. However, previously reported works mainly focused on batch learning. It is not clear how much impact the adversarial attack will have on the deep learning model when performing incremental learning tasks. This issue exposes the hidden safety risks of unmanned driving and increases discuss opportunities. Therefore, we propose an adversarial attack based on incremental learning techniques for unmanned scenes in this paper. Specifically, it can retain information previously learned by the model. At the same time, it can renew the old model to learn new model, thereby continually adding small perturbation to legitimate examples. A couple of experiments on the Pascal VOC 2012 dataset has been conducted, and the experiment results show that the adversarial attack based on incremental learning techniques has a higher attack success rate. Further, it can improve the successful attack rate by 8.43%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".