Exploring Backdoor Vulnerabilities and Fairness Challenges in Generative Models: A Survey

Global connectivity has increased societal diversity worldwide, fostering growth and dispelling negative stereotypes. However, ensuring equality remains challenging, with some groups favoured and others facing unequal opportunities. The evolution of Artificial Intelligence (AI) promises automation, innovation, and industry transformation. Yet, as AI becomes pervasive, establishing trust in algorithmic decision-making is crucial to eliminate bias and ensure fair outcomes. Adversarial attacks, particularly backdoor attacks, pose significant threats to AI systems. Research must focus on mitigating discrimination and bias in AI models, emphasising ethical considerations and model interpretability. Accordingly, this paper surveys the critical aspects of fairness, bias, and backdoor attacks in AI systems, integrating significant and state-of-the-art research. This paper begins by defining key concepts, including fairness, bias, and backdoor attacks, and exploring related elements. A thorough survey highlights key studies on fairness and bias analysis, metrics for measurement, and various fairness attacks. This paper further examines methodologies for performing backdoor attacks and their implications for AI systems. Additionally, this paper explores the latest defence mechanisms addressing fairness and bias issues and mitigating backdoor attacks, showcasing advancements in safeguarding AI integrity. The survey concludes with discussions on popular datasets, future research directions, and potential limitations in ongoing developments. This is the first study combining surveys of backdoor attacks and fairness in AI models to examine security vulnerabilities that can lead to bias susceptibility and unfair outcomes. By consolidating crucial research insights, this survey provides a comprehensive understanding of the challenges and progress in ensuring fairness and security in AI systems, offering valuable perspectives for researchers and practitioners.