Human Action CLIPS: Detecting AI-generated Human Motion

Full-blown AI-generated video generation continues its journey through the uncanny valley to produce content that is perceptually indistinguishable from reality. Intermixed with many exciting and creative applications are malicious applications that harm individuals, organizations, and democracies. We describe an effective and robust technique for distinguishing real from AI-generated human motion. This technique leverages a multi-modal semantic embedding, making it robust to the types of laundering that typically confound more low- to mid-level approaches. This method is evaluated against a custom-built dataset of video clips with human actions generated by seven text-to-video AI models and matching real footage.

DeepSpeak Dataset v1.0

We describe a large-scale dataset--{\em DeepSpeak}--of real and deepfake footage of people talking and gesturing in front of their webcams. The real videos in this first version of the dataset consist of $9$ hours of footage from $220$ diverse individuals. Constituting more than 25 hours of footage, the fake videos consist of a range of different state-of-the-art face-swap and lip-sync deepfakes with natural and AI-generated voices. We expect to release future versions of this dataset with different and updated deepfake technologies. This dataset is made freely available for research and non-commercial uses; requests for commercial use will be considered.

Nepotistically Trained Generative-AI Models Collapse

Trained on massive amounts of human-generated content, AI (artificial intelligence) image synthesis is capable of reproducing semantically coherent images that match the visual appearance of its training data. We show that when retrained on even small amounts of their own creation, these generative-AI models produce highly distorted images. We also show that this distortion extends beyond the text prompts used in retraining, and that once poisoned, the models struggle to fully heal even after retraining on only real images.