👉 Cheat engineering refers to the development and use of sophisticated, often proprietary algorithms and techniques designed to circumvent the rules or constraints imposed by machine learning models, particularly in adversarial settings. These methods aim to manipulate model inputs or outputs in subtle ways that can cause misclassification or undesired behavior without directly altering the model's architecture or training data. Cheat engineers craft inputs that exploit vulnerabilities in the model's decision-making process, such as carefully crafted perturbations to text or images that lead to incorrect predictions while remaining imperceptible to humans. This practice is prevalent in security-sensitive applications, like image recognition and natural language processing, where adversaries seek to exploit model weaknesses for malicious purposes, such as bypassing authentication systems or generating deceptive content.
