To further stress-test the limits of provable defenses against adversarial corruptions, researchers can explore the development of targeted and backdoor poisoning attacks in addition to untargeted attacks.
Targeted Poisoning Attacks: Targeted attacks aim to manipulate the model's behavior to misclassify specific classes of inputs. By crafting poisoned data samples that are strategically designed to cause misclassification of specific target classes, targeted poisoning attacks can challenge the robustness of machine learning models. These attacks can be used to evaluate the effectiveness of defense mechanisms in detecting and mitigating class-specific biases introduced by adversarial manipulations.
Backdoor Poisoning Attacks: Backdoor attacks involve inserting hidden triggers or patterns into the training data that can be exploited to trigger specific behaviors in the model during inference. By injecting subtle but malicious patterns into the training data, backdoor attacks can compromise the integrity and reliability of machine learning models. Evaluating the resilience of defenses against backdoor attacks can provide insights into the robustness of the models against covert manipulations.
By exploring these different types of poisoning attacks, researchers can comprehensively assess the effectiveness of provable defenses in safeguarding machine learning models against various adversarial threats and vulnerabilities.