Manual Modification
Manual modification is when someone tries to trick an AI by changing small parts of the information they feed it. Think of it like someone trying to find the exact right way to scribble on a stop sign so that a self-driving car misreads it as a speed limit sign, testing different marks until the car gets it wrong.
Manual modification refers to the deliberate, human-guided alteration of input features to induce model misclassification. Practitioners leverage their understanding of the model's decision boundaries to iteratively perturb input data, testing various modifications until the desired adversarial outcome is achieved.
Manual modification is an adversarial process wherein an agent performs targeted, non-automated perturbations to input data to exploit model vulnerabilities. By utilizing domain knowledge or white-box access to the target model's architecture, the adversary iteratively refines input features to maximize the probability of an adversarial misclassification, validating the efficacy of the crafted input through empirical trial-and-error cycles.