{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Good Tech Companies ","title":"Can Voice Deepfake Detection Keep Up With the 1600% Surge in Fraud Attacks?","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/92865d1e\"></iframe>","width":"100%","height":180,"duration":984,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/can-voice-deepfake-detection-keep-up-with-the-1600percent-surge-in-fraud-attacks.\nDeepfake vishing surged 1600% in 2025. Most detection tools analyze text, not audio — and miss what matters. Here's how voice-native detection actually works.\nCheck more stories related to cybersecurity at: https://hackernoon.com/c/cybersecurity.\n            You can also check exclusive content about #fraud-detection, #voice-fraud, #enterprise-security, #audio-native-detection, #vishing-attacks, #synthetic-voice-fraud, #spectrogram-analysis, #good-company,  and more.\nThis story was written by: @modulate. Learn more about this writer by checking @modulate's about page,\n            and for more stories, please visit hackernoon.com.\nDeepfake fraud is accelerating and most detection systems are built wrong — they analyze transcripts instead of raw audio, missing the synthetic speech artifacts that actually reveal a fake. This piece breaks down how attacks work in production, why text-first detection fails, and how audio-native models like Modulate's Deepfake Detection API catch what humans and biometrics can't.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/HZ9CRzf5js9DK86xzUVMWBRbXYwg4dA8xVXJGVzpL6Y/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8xMTNl/MjgwMmI0ZmEzNThj/YmJiOWNiN2UyZmRm/MzY3My5qcGVn.webp","thumbnail_width":300,"thumbnail_height":300}