Welcome!

Here you will find code to reproduce the results for the paper "Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons" in 7th IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF).

Dataset

It is necessary to sign a license agreement to get access to the data, you can find the agreement here. Please sign it and send a copy to Sandra Avila sandra@ic.unicamp.br.

The dataset comes with specified training/validation splits. In the link you find:

• The Elsagate videos downloaded from YouTube: All videos from the training set were cut to have at maximum 3 minutes and 15 seconds. For the validation set, we used the full videos to validate the filtering method as it would work in production.
• The extracted and processed frames and motion vectors. The preprocessing steps are explained in the next section.
• The two folds in the training set used in the experiments.

Data Annotation

Please note that we did not perform a manual data annotation. Videos downloaded from official channels (e.g., Disney Channel, Cartoon Network) were considered safe and those downloaded from channels considered Elsagate in the r/Elsagate subreddit were labeled as Elsagate.

Methodology Pipeline

Access specific instructions for each step of the pipeline through the links below:

1. Data Preprocessing: Extract/Generate low-level data (static and/or motion)
2. Models: See our trained models to execute a feature extraction.
3. Feature Extraction: Use a Deep Learning Architecture (DLA) model to extract the features from the low-level data and pool the features into a single global descriptor of the video.
4. Classification: Predict the class of the video through frames and motion vectors individually using SVM and fusion the frames and motion vectors scores to get a final classification.
5. Finetuning: Use our scripts to finetune one of our neural networks for your data.

Citation

If this work/repository was useful for your project, please consider citing our paper.

@inproceedings{ishikawa2019dlaelsagate,
 title={Combating the {Elsagate} Phenomenon: {D}eep Learning Architectures for Disturbing Cartoons},
 author={Akari Ishikawa and 
         Edson Bollis and 
         Sandra Avila},
 booktitle={7th IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF)},
 year={2019}
}

Also, our work was largely based on Mauricio Perez work: Video pornography detection through deep learning techniques and motion information. We strongly recommend the reading if you are planning on reproducing our experiments.

Acknowledgments

• A. Ishikawa is funded by PIBIC/CNPq, FAEPEX (#2555/18) and Movile.
• E. Bollis is funded by CAPES.
• S. Avila is partially funded by Google Research Awards for Latin America 2018, FAPESP (#2017/16246-0) and FAEPEX (#3125/17).
• RECOD Lab. is partially supported by diverse projects and grants from FAPESP, CNPq, and CAPES.
• We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPUs used for this research.