Data poisoning is an adverse attack that attempts to influence the training database to control the forecast function of a trained model, e.g., identifying any unusual activity as safe. Without affecting the training process, a data poisoning assault can technically build easy-to-implement backdoors.
The relationship between data poisoning and backdoor assaults is that poisonous data can be introduced into the attacked system via the backdoor. Data poisoning and backdoor attacks also modify training data to make models malfunction during the assessment.