Fit for Purpose? Deepfake Detection in the Real World
This work addresses the critical issue of misinformation from political deepfakes for the public, highlighting the limitations of current detection methods in real-world settings, which is incremental as it benchmarks existing approaches rather than proposing a new solution.
The paper tackled the problem of deepfake detection in real-world political contexts by evaluating state-of-the-art detectors on a curated database of authentic political deepfakes from social media, finding that all detectors, including paid tools, struggle to generalize effectively and are vulnerable to simple manipulations.
The rapid proliferation of AI-generated content, driven by advances in generative adversarial networks, diffusion models, and multimodal large language models, has made the creation and dissemination of synthetic media effortless, heightening the risks of misinformation, particularly political deepfakes that distort truth and undermine trust in political institutions. In turn, governments, research institutions, and industry have strongly promoted deepfake detection initiatives as solutions. Yet, most existing models are trained and validated on synthetic, laboratory-controlled datasets, limiting their generalizability to the kinds of real-world political deepfakes circulating on social platforms that affect the public. In this work, we introduce the first systematic benchmark based on the Political Deepfakes Incident Database, a curated collection of real-world political deepfakes shared on social media since 2018. Our study includes a systematic evaluation of state-of-the-art deepfake detectors across academia, government, and industry. We find that the detectors from academia and government perform relatively poorly. While paid detection tools achieve relatively higher performance than free-access models, all evaluated detectors struggle to generalize effectively to authentic political deepfakes, and are vulnerable to simple manipulations, especially in the video domain. Results urge the need for politically contextualized deepfake detection frameworks to better safeguard the public in real-world settings.