It can’t learn and apply principles about a generic pose ‘for good and all’, but needs abundant examples in the training dataset, for each and every identity that is going to be involved in the face-swapping. The encoder/decoder system then compares every single image in each folder to each other, sustaining, improving and repeating this operation for hundreds of thousands of iterations (often for as long as a week), until it understands the essential characteristics of both identities well enough to swap them around at will.įor each of the two people being swapped in the process, what the deepfake architecture learns about identity is entangled with context. Note the distinctive expression at the end of the third row in column A, which is unlikely to have a close equivalent in the other dataset. What Is Holding Back Current Deepfake Technology?ĭeepfakes are currently created by training an encoder/decoder machine learning model on two folders of face images – the person you want to ‘paint over’ (in the earlier example, that’s Arnie) and the person you want to superimpose into the footage (Sly).Įxamples of varying pose and lighting conditions across two different face-sets. To understand what the work means, let’s take a look at how deepfakes are currently produced everywhere from deepfake porn sites to Industrial Light and Magic (since the DeepFaceLab open source repository is currently dominant in both ‘amateur’ and professional deepfaking). The new paper is titled Delta-GAN-Encoder: Encoding Semantic Changes for Explicit Image Editing, using Few Synthetic Samples, and comes from researchers at Technion – Israel Institute of Technology. Rather, this represents a way forward for a fundamental separation of instrumentality (such as ‘change the angle of the head’, ‘create a frown’) from identity, offering a path to a high-level rather than ‘derivative’ image synthesis-based deepfake framework.

This is not mere deepfake head-puppetry, a technique more suitable for avatars and partial-face lip-synching, and which has limited potential for full-fledged deepfake video transformations. Bottom right, we see both characteristics applied simultaneously.

In the row below, we see the stretched mouth corner facet transferred onto the former president. In the top row, we see a ‘wink’ transferred onto the identity of Barack Obama, prompted by the learned nonlinear path of a GAN’s latent space, represented by the CGI image on the left. winking an eye) from the individual’s identity encoding, using unrelated synthetic face data (pictured left). The new system discretely separates pose and context (i.e.

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