AI ML DL

aesthetic-diffusion-MAIN-IMAGE
AI ML DL

Custom Styles in Stable Diffusion, Without Retraining or High Computing Resources

A researcher from Spain has developed a new method for users to generate their own styles in Stable Diffusion (or any other latent diffusion model that is publicly accessible) without fine-tuning the trained model or needing to gain access to exorbitant computing resources, as is currently the case with Google’s DreamBooth and with Textual Inversion – both methods which are primarily intended to insert objects or people into the Stable Diffusion universe, rather than impose environmental ambience or styles (i.e. ‘in the style of Van Gogh/Kubrick/Mapplethorpe’, etc.).

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deepfake body
AI ML DL

The Road to Realistic Full-Body Deepfakes

It’s nearly five years since the advent of deepfakes released into the public realm the ability to alter people’s facial identities; at first, in recorded video, and now even as a streaming implementation, with DeepFaceLive.

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AI ML DL

Stable Diffusion: Is Video Coming Soon?

For an excited public, many of whom consider diffusion-based image synthesis to be indistinguishable from magic, the open source release of Stable Diffusion seems certain to be quickly followed up by new and dazzling text-to-video frameworks – but the wait-time might be longer than they’re expecting.

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Trump Navalny
AI ML DL

Detecting Deepfakes Through Personality Assessment

In the original, and the various cinematic incarnations of Jack Finney’s 1954 science-fiction novel The Body Snatchers, the fact that aliens are ‘taking over’ the earthly population is signaled to their loved ones by some imperceptible changed quality that cuts through the perfect simulation of their form.

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Defeating Deepfaked Video Calls by Monitoring Electrical Fluctuations
AI ML DL

Defeating Deepfaked Video Calls by Monitoring Electrical Fluctuations

2022 has seen the emergence of ‘live’ deepfakes in videoconferencing as an acknowledged security threat. In June, the FBI issued a warning to businesses about the increasing use of deepfake technologies to apply for remote work positions; in July, also in the United States, the Better Business Bureau (BBB) warned against the use of deepfakes as an enabler for a new and unsettling strain of fraudulent and criminal phishing and social engineering attacks.

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The Future of Generative Adversarial Networks in Deepfakes
AI ML DL

The Future of Generative Adversarial Networks in Deepfakes

Excluding ‘traditional’ CGI methods, which date back to the 1970s, there are currently three mainstream AI-based approaches to creating synthetic human faces, only one of which has attained any widespread success or societal impact: autoencoder frameworks (the architecture behind current viral deepfakes); Generative Adversarial Networks (GANs); and Neural Radiance Fields (NeRF).

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Autoencoder - Future
AI ML DL

The Future of Autoencoder-Based Deepfakes

The way we refer to visual effects (VFX) work may be changing soon. For instance, at the time of writing, the relatively new technology of Neural Radiance Fields (NeRF) makes it possible to recreate entire scenes – including humans, homes, exterior environments, and almost anything else you can imagine – inside an AI’s neural network, from a handful of static photos.

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