This presentation delves into my new research and methodologies for attacking Deep Neural Networks (DNNs) and AI models in black-box environments (without access to internal parameters).
Traditionally, adversarial attacks require access to the model's internals (white-box access), limiting their application in black-box settings. However, this talk introduces **two innovative techniques** to bypass this restriction. Attendees will gain a deep understanding of how these techniques work, from identifying a model's architecture through **model enumeration** to adapting **white-box attack strategies** for black-box models.