: Many prevalent "sketching" algorithms used in data analytics suffer from adversarial attacks, whereas importance-sampling-based methods have shown more resilience. The Path to Reliability: Defenses & Frameworks
Building trustworthy AI requires moving beyond standard accuracy and focusing on . Key strategies currently being explored include: : Many prevalent "sketching" algorithms used in data
The following draft explores the critical intersection of and signal processing , inspired by current research like the text Machine Learning Algorithms: Adversarial Robustness in Signal Processing by Springer . Adversarial robustness is the ability of a model
Adversarial robustness is the ability of a model to resist being fooled by "adversarial examples"—carefully crafted inputs that appear normal to humans but cause ML models to make catastrophic errors. A slight, imperceptible perturbation to a signal can flip a 91% confident "pig" classification to a 99% confident "airliner". : Many prevalent "sketching" algorithms used in data
Recent studies highlight that foundational signal processing tasks are surprisingly vulnerable to data poisoning and feature modification: