Multi-category sensitive image recognition based on RefCA-EfficientNetV2
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
In an era where the Internet has become a vital component of our daily existence, an unforeseen explosion of information has surfaced, including sensitive images containing pornographic, political, and horror themes. This content, threatening our well-being and polluting the Internet environment, compels the necessity for effective filtering and identification measures. Existing multi-category sensitive image detection methods, however, grapple with issues like scale, significant inference time, and unsatisfactory accuracy. In response to these challenges, we present RefCA-EfficientNetV2, a novel method built on the EfficientNetV2 model. This innovative solution enhances channel correlations and incorporates Coordinate Attention, thereby refining spatial coordinate information for eased region localization. Demonstrating marked accuracy improvement, our method attains a remarkable 97.64% accuracy level on sensitive images. With minimal parameter increase and time, RefCA-EfficientNetV2 not only enhances multi-category image accuracy but significantly reduces computational amount, offering a robust framework for cyberspace governance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it