The rise of generative AI presents a profound duality. On one hand, it offers a powerful solution to data scarcity and privacy challenges in biometrics. On the other, it is weaponized to create deepfakes that threaten digital integrity. Existing detectors for these deepfakes are brittle, failing against real-world transformations and novel generative models. This dissertation confronts this duality head-on. First, I establish the viability of synthetic data for building fair and private biometric systems. Second, to counter the malicious use of this technology, this dissertation develops deepfake detectors designed to be robust, generalizable, and efficient by construction. My work introduces novel, lightweight feature sets on different cues (e.g., colour cue-based Relative Chrominance Difference, Gradient features, Depth cues, etc.) that are inherently resilient to OSN transformations and improve generalisation to unseen forgeries. Whereas, accomplished results confirm state-of-the-art performance, achieving high accuracy in challenging real-world scenarios with a significant reduction in model complexity, my current and future work focuses on achieving superior generalisation while being OSN manipulation resistant.