Shama AI develops real-time audio deepfake detection models using lightweight deep learning algorithms to identify and mitigate voice spoofing attacks with 99.9% accuracy. The technology addresses the challenge of indistinguishable AI-generated voices, ensuring the integrity of voice communications and protecting user identities from cyber threats.
Funding
Funding not disclosed
Founders
Product
Problem
The increasing sophistication and accessibility of AI voice cloning technologies make it difficult to distinguish between real and fake voices, creating vulnerabilities to voice spoofing attacks. Current security technologies often fail to detect these advanced AI-generated voice attacks in real-time, leading to potential damage before detection occurs.
Solution
Shama AI offers a real-time audio deepfake detection model designed to identify and mitigate voice spoofing attacks. Using lightweight deep learning algorithms and optimized Digital Signal Processing, the technology analyzes audio streams to detect AI-generated voice manipulation with high accuracy. The solution provides real-time detection and response capabilities for both streaming and uploaded media, operating with low computational resources. Shama AI prioritizes privacy by not collecting or comparing personal voice samples, ensuring user data remains secure.
Target Audience
Shama AI targets B2B clients across various sectors, including telecom, speaker and voice recognition, BFSI, defense and government, public sector and healthcare, and media and entertainment.
Features
- Universal, scalable, and adaptable anti-spoofing model
- Real-time detection and response in milliseconds for streaming and uploaded media
- Optimized Digital Signal Processing algorithms for low memory usage
- Privacy-focused design that avoids collecting or comparing personal voice samples
- Deployment options for embedded systems to ensure security at the hardware level
- High accuracy (99.9%) in detecting deepfakes, cyberattacks, and generative AI manipulations