
Reality Defender

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About
Reality Defender is a multi-model and multimodal deepfake detection platform that helps enterprises and governments detect AI-generated content at scale. It offers solutions for various industries including media, finance, and government. It analyzes audio, video, images, and text, providing risk scoring, email alerts, and reporting. The platform is designed to protect against synthetic images, voice clones, video manipulation, and AI-generated text. It is used for contact centers, brand monitoring, content moderation and disinformation detection.
Platform
Task
Features
• risk scoring
• platform agnostic integration
• email alerts and reporting
• analysis of audio, video, images, and text
• detection of ai-generated voice clones
• real-time analysis of live calls
• detects ai-generated content at scale
• multi-model and multimodal deepfake detection
FAQs
What is Reality Defender?
Reality Defender is a deepfake detection platform for enterprises, governments, and platforms to detect AI-generated content and manipulations across audio, video, image, and text files.
Can anyone use Reality Defender?
No. Reality Defender is meant for the largest entities and governments to potentially shield hundreds of millions from deepfakes. We believe the onus of deepfake detection should not be on the end-user, consumer, or citizen, but on the largest institutions and platforms serving them.
Does Reality Defender Use Watermarking?
No. Watermarking requires provenance and ground truth, which requires buy-in from all generative content models. As such a feat is impossible, we use an inference system that grades each content with a 1-99% probability rating — all without the need for the ground truth.
Is the Generative AI technology that you are leveraging proprietary, or is it provided by a third party?
We are not leveraging Generative AI technologies. Our detection models are a combination of in-house models or proprietary models.
What are Reality Defender’s certifications?
The platform has a range of US, UK, and EU certifications and is currently completing SOC2 and GDPR certifications.
What data/algorithms do you use to train your detection tools?
We use state-of-the-art neural networks (CNNs, Transformers, ViTs, including large foundation models) to model discriminating features that help differentiate generative media from real media. In this process, we perform spatial, temporal, and frequency domain analysis along with domain specific feature losses (such as artifacts in images, etc.). Furthermore, we support multiple models for various modalities. We have created a diverse in-house dataset consisting of videos, image, audio (including telephone quality) and text.
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