An Adaptive Porn Video Detection Based on Consecutive Frames Using Deep Learning
Bibliographic record
Abstract
Many videos uploaded to online video platforms contain adult content that violates these platforms' policies and should be removed immediately.To recognize obscene videos, we developed a model that can process video frames in real-time while also adapting to time budget or hardware processing capacity.Thus, a deep convolutional neural network with multiple outputs was used.A decision-maker module was then designed to decide which neural network outputs to process and which label to assign to each frame.Using the reinforcement learning method, the decision-maker module is trained based on the results of previous frames as well as the results of neural network outputs while keeping the time budget in mind.Experiments showed that sacrificing a small amount of accuracy can increase speed by up to 4.7 times over the base model.We conclude that using a content correlation between consecutive frames not only reduces processing time by eliminating unnecessary frame processing but also improves the accuracy of the frame classification.It was also discovered that while using more of the previous frames, increases processing speed, the error in classifying the frame increases when the scene is changed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".