Q&A between researchers who are improving deepfake detection
In the real world, attackers don't just generate a selfie, submit it, and move on. If one attempt fails, they’ll refine the image and try again. Existing datasets often fail to account for these variations, leading the researchers’ detection models to fall short in real-world scenarios. Zhaofeng Si, a PhD student at University at Buffalo who interned at Persona as a research scientist, and Saatvik Billa, an ML engineer and researcher at Persona, worked together this summer on a fraud taxonomy and a real-world selfie fraud benchmark.