Spectral Domain Attack

Spectral domain attacks exploit the vulnerabilities of deep learning models by manipulating the frequency representation of input data, rather than directly altering the spatial domain. Current research focuses on developing effective attack algorithms across various data types, including images, audio, and 3D meshes, often leveraging gradient-based methods and graph signal processing techniques to craft imperceptible yet highly effective adversarial examples. These attacks highlight the fragility of deep learning systems and drive the development of robust defenses, with implications for the security and reliability of applications ranging from speech recognition to autonomous driving.