Evaluating the Adversarial Robustness of Deployed AI Models
Deep learning vision models are known to be vulnerable to adversarial examples, where small, optimized input perturbations can cause confident mispredictions. A common objection is that such attacks work only in the digital domain and do not survive real-world conditions. In this talk, we show that gradient-based white-box attacks extend to deployed systems in three […]