To submit your method, you should send an email to macedo.jjmn@dpf.gov.br with a zip file containing your method and a script for a docker container, defining libraries and dependencies. Alternatively, you can email a link for these files.

You should also implement the code to test your method, with the following requirements:

• - assume that the images are located in a folder named "data", under your method's directory;
• - output a csv file named results.txt, containing the name of each tested image and your prediction ('True' or 'False') for child pornography.

 
filename001.jpg,True
filename002.jpg,False
filename003.jpg,False

The following script shows an example of this code in Python.

 
import os
import random
from os.path import abspath, dirname, isfile, join

# predicts whether an image is related to child pornography
# returns True or False
def predict(file_path):
    #call your method here
    return random.choice([True, False])

if __name__== "__main__":
    script_dir = dirname(abspath(__file__))
    dset = join(script_dir, "data")
    results = join(script_dir, "results.txt")
    output = open(results, "w")
    fnames = [f for f in os.listdir(dset) if isfile(join(dset, f))]
    for f in fnames:
        res = predict(join(dset, f))
        output.write(f + "," + str(res) + "\n")
    output.close()

Results of methods and tools evaluated against the RCPD dataset:

The dataset is described in the paper A Benchmark Methodology for Child Pornography Detection.

If you mention the RCPD dataset or submit your method to evaluation, please use the following reference:

 
@inproceedings{
  macedo2018rcpd,
  title={A Benchmark Methodology for Child Pornography Detection},
  author={J. Macedo, F. Costa and J. A. dos Santos},
  booktitle={Graphics, Patterns and Images (SIBGRAPI), 2018 31th SIBGRAPI Conference on},
  year={2018},
  organization={IEEE}
}