Facebook, AWS, Microsoft and others launch coding challenge for deepfake detection

Facebook announced a challenge to develop technologies for detecting deepfakes, titled the Deepfake Detection Challenge (DFDC). The announcement took place at NeurIPS 2019 (Conference on Neural Information Processing Systems), Dec 8-14, 2019.

Deepfakes are AI-generated videos that can falsely depict people saying or doing things that didn’t happen, and it’s not hard to imagine what harm this may cause. Beside the tech giants Facebook, Microsoft and Amazon Web Services (AWS), the challenge was also developed by The Partnership on AI Steering Committee on AI and Media Integrity and academics.

Let’s take a look at the requirements and prizes.

SEE ALSO: Generative Adversarial Networks: “GANs can create new ‘realities’ that never existed”

Deepfake Detection Challenge requirements

The competition is carried out via DFDC’s Kaggle page. It comes with four groups of datasets, namely a training set available outside Kaggle, a public validation set within Kaggle, a public test set that is completely withheld and a private test set that is privately held outside Kaggle.

The DFDC team offers some tips, e.g. ensuring the code can be re-run on any unseen test set, and warns not to create multiple accounts as these users will be excluded from participating.

Submissions must be made through Kaggle Notebooks, Kaggle’s cloud computational environment for machine learning code. Be sure to read the full instructions, as the workflow is claimed to be unique for this competition.

Deadlines and prizes

The entry deadline is March 3, 2020, which also marks the last day participants may join or merge teams. The final deadline for submissions is March 31, 2020. All deadlines are at 11:59:00 PM UTC on the respective day. The prizes for the five highest-ranked projects range from $500,000 USD to $40,000 USD.

SEE ALSO: Generative Adversarial Networks: “GANs can create new ‘realities’ that never existed”

More details on the Deepfake Detection Challenge can be found on Kaggle and Medium.

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