ContentChecker
Why use deepfake detection?
The audio deepfake detector checks whether audio has been generated by artificial intelligence or by a person
Audio deepfake detection
Our deepfake detector allows you to detect Artificial intelligence (AI) generated sounds such as phone calls, video calls, or deepfakes. It uses a unique approach combining a range of techniques and can detect ai generated audio in real-time, identify the model used and can detect ai generated audio from ‘unseen models’.
Our approach is world leading and has very low error rates compared to other approaches. It also detects AI generated sounds that has been manipulated by people or with online tools such as voice cloning. Our AI sound detector also looks at the text analysis to decide whether the audio has been scripted by AI.
Visualisation Tool
AI Aware’s visualisation tool shows the proportion of AI-generated content and whether it occurs in blocks or is dispersed. This allows you to contextualise the findings and understand the significance of AI-generated input.
Our AI sound detector grades each section of audio as AI or human generated content, showing its consistency with either. This means that users can get an overall view of a document and understand what is AI and what is human created.
Benefits of deepfake detection for companies
AI-generated audio can be used for social engineering attacks, and be used to spread misinformation.
Video calls are increasingly being used to scam businesses out of millions of pounds.
our AI audio detector allows you to identify AI-generated content over video calls with a high degree of accuracy. We have tested on large quantities of varied output to ensure that our false positive rate is orders of magnitude lower than other products.
Using an AI audio detector over video calls can flag any potential scammers and avoid financial loss.
Market leading audio deepfake detection
AI Aware has built it’s audio deepfake detector combining unique non-machine learning approaches with large scale machine learning to identify the differences between AI-generated and human created sounds.
We use various signals from human and AI content such as logic, linguistic structure and creativity to differentiate between the two.