Bibliographic record
Abstract
Object detection, a fundamental element of computer vision and artificial intelligence, has experienced considerable advancements through the incorporation of deep learning-based techniques. Yet, despite the impressive strides in both accuracy and efficiency, object detection algorithms harbor inherent vulnerabilities to adversarial attacks. These well-crafted disruptions pose significant risks, especially considering the broad application of object detection across an array of safety-critical sectors such as autonomous transportation, medical imaging, and security systems. This comprehensive paper offers a thorough review of adversarial attacks against object detection systems, dissecting the methods employed, and scrutinizing the implications of their exploits. It dives deep into the mechanics and consequences of both white-box and black-box attacks on prevalent object detection networks, including but not limited to Faster R-CNN, YOLO, and SSD. Furthermore, this paper underscores an assortment of defense strategies developed to mitigate the effects of adversarial attacks. These include adversarial training, gradient masking, input transformations, and randomized defenses. While these strategies hold promise, it is acknowledged that they have their limitations and do not offer a universal solution against all adversarial attacks. As such, this paper underscores the urgent necessity for robust defense mechanisms and stimulates further discourse and investigation into developing truly resilient object detection systems, capable of withstanding the growing threat of adversarial attacks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".