Backdoor Effect

Backdoor attacks exploit vulnerabilities in machine learning models, surreptitiously embedding malicious behavior triggered by specific inputs. Current research focuses on developing robust defenses against these attacks, particularly within diffusion models, contrastive learning frameworks, and vision transformers, employing techniques like unlearning, neural distribution tightening, and structural pruning to mitigate the backdoor effect. This is a critical area of study due to the increasing reliance on machine learning in security-sensitive applications, with effective defenses crucial for maintaining model integrity and trustworthiness.