As Terminator was followed by Terminator 2, Paravision is amping up its deepfake detection product with Deepfake Detection 2.0 – what a release calls “a major evolution” in Paravision’s identity fraud prevention suite.
A major upgrade is a new ability for the detection tool to recognize synthetic faces generated by advanced AI models, such as advanced diffusion and GAN technologies, in addition to traditional deepfakes such as identity and expression swaps. The model claims to offer 97 percent improvement in error rates compared to version 1.0.
The biometric deepfake detection system analyzes facial imagery and generates a score gauging the likelihood that the image is authentic. Images scoring below a configured threshold are flagged as deepfakes. The tech also provides scores for specific types of manipulation, for more in-depth analysis of the prevalence and nature of deepfake attack vectors.
Doug Aley, CEO of Paravision, says Deepfake Detection 2.0 adds another building block to its fortress of fraud prevention products, which also includes facial recognition and liveness detection. “We’re giving our partners the ability to detect not just who someone is, but whether the image itself is real. As AI-generated content becomes more sophisticated, our goal is to stay ahead of the threat curve and provide the most complete, secure and future-ready identity infrastructure on the market.”
Paravision Chief Product Officer Joey Pritikin agrees: “as threats continue to evolve, so will we.”
The new deepfake detection toolkit is available through Paravision’s cloud-ready SDKs and Docker-based architecture, making it easy to deploy for digital onboarding, secure access, travel identity, and remote verification environments.
Paravision recently launched Liveness 2.0, an update to its liveness detection solution, which among 15 selected to represent the state of the art by the U.S. Department of Homeland Security’s Science and Technology Directorate (S&T).
Article Topics
biometrics | deepfake detection | deepfakes | fraud prevention | generative AI | Paravision | synthetic data
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