Repurposing Neural Networks to Generate Synthetic Media for Information Operations

Using open source pre-trained natural language processing, computer vision, and speech recognition neural networks, we demonstrate the relative ease with which fine tuning in the text, image, and audio domains can be adopted for generative impersonation. We quantify the effort involved in generating credible synthetic media, along with the challenges that time- and resource-limited investigators face in detecting generations produced by fine-tuned models. We wargame out these capabilities in the context of social media-driven information operations, and assess the challenges underlying detection, attribution, and response in scenarios where actors can anonymously generate and distribute credible fake content. Our resulting analysis suggests meaningful paths forward for a future where synthetically generated media increasingly looks, speaks, and writes like us.

By Philip Tully

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