Deep Privacy Aims to Anonymize People While Retaining Expressions - vasquezviess1990

One of the most important concerns in deepfakes is undoubtedly privacy, which is exactly what these researchers at the Norwegian University of Science and Technology has tried to fix.

This modern technique named Deep Privacy makes use of fruitful adversarial networks(GAN), the inherent technology employed in Deep fakes to anonymize the subjects while replicating their characteristics.

The algorithms used in Deep Privacy extract critical facial expressions from the subjugate and runs it through the database trained with 1.5 million face images to get a new face retaining all the details from the source image.

For accurately detecting facial features, the researchers use Masquerade R-CNN and Dual Colorful Look Sensing element (DSFD). Mask R-CNN is responsible for generating sparse pose information of the font while DSFD is victimized for detecting the faces portray in the visualize.

"We present experimental results reflecting the capability of our model to anonymize images while preserving the data dispersion, making the data suitable for boost training of deep learning models. As far as we know, no other solution has been proposed that guarantees the anonymization of faces spell generating realistic images.", wrote the researchers.

Since the project is highly research at this point, you should not be expecting progressive results here. From the implementation, however, IT appears like it could improve significantly over time.

The source code of the research newspaper publisher is available on GitHub. If you're interested to run over the code yourself, you May do then from here. The instructions to set ascending the surroundings are given in the README.md register in the repository as well. You can besides range the project in Google Colab from here.

So, what get along you think about Deep Privacy? Let United States know your thoughts in the comments.