Sarah Barrington

Sarah Barrington, Maty Bohacek, Hany Farid

We present DeepSpeak-Agentic, a dataset of videos comprising over 37 hours of semi-structured conversations between a human and an embodied AI agent. We use this dataset to evaluate the automatic forensic identification (audio, video, or text) of AI agents, study the nature of human-agent interactions, and provide a benchmark for future advances in the large-language models and AI-generated voices and faces that power embodied AI agents. We also contribute a scalable data-capture system that creates agents, automatically pairs them with human crowd workers, records audiovisual conversations across specified scenarios, and identifies and separates the human and agent in the combined stream.

Sarah Barrington, Romit Barua, Gautham Koorma et al.

Synthetic-voice cloning technologies have seen significant advances in recent years, giving rise to a range of potential harms. From small- and large-scale financial fraud to disinformation campaigns, the need for reliable methods to differentiate real and synthesized voices is imperative. We describe three techniques for differentiating a real from a cloned voice designed to impersonate a specific person. These three approaches differ in their feature extraction stage with low-dimensional perceptual features offering high interpretability but lower accuracy, to generic spectral features, and end-to-end learned features offering less interpretability but higher accuracy. We show the efficacy of these approaches when trained on a single speaker's voice and when trained on multiple voices. The learned features consistently yield an equal error rate between 0% and 4%, and are reasonably robust to adversarial laundering.

Sarah Barrington, Matyas Bohacek, Hany Farid

Deepfakes represent a growing concern across domains such as impostor hiring, fraud, and disinformation. Despite significant efforts to develop robust detection classifiers to distinguish the real from the fake, commonly used training datasets remain inadequate: relying on low-quality and outdated deepfake generators, consisting of content scraped from online repositories without participant consent, lacking in multimodal coverage, and rarely employing identity-matching protocols to ensure realistic fakes. To overcome these limitations, we present the DeepSpeak dataset, a diverse and multimodal dataset comprising over 100 hours of authentic and deepfake audiovisual content. We contribute: i) more than 50 hours of real, self-recorded data collected from 500 diverse and consenting participants using a custom-built data collection tool, ii) more than 50 hours of state-of-the-art audio and visual deepfakes generated using 14 video synthesis engines and three voice cloning engines, and iii) an embedding-based, identity-matching approach to ensure the creation of convincing, high-quality identity swaps that realistically simulate adversarial deepfake attacks. We also perform large-scale evaluations of state-of-the-art deepfake detectors and show that, without retraining, these detectors fail to generalize to the DeepSpeak dataset. These evaluations highlight the importance of a large and diverse dataset containing deepfakes from the latest generative-AI tools.