Robust Deepfake Detection by Addressing Generalization and Trustworthiness Challenges: A Short Survey

Rapid progress in deep learning technologies has sparked a surge in deepfake content, creating significant risks to security, privacy, and trust in digital information. This paper explores the evolution of robust deepfake detection methods over recent years from two perspectives, specifically, generalized and trustworthy detection. As numerous generative techniques continuously emerge, generalized detection emphasizes the robustness against data distribution shift represented by unseen manipulation at testing time. By systematically reviewing generalized detection methods, we categorize these approaches into input-level, model-level, and learning-level. Trustworthy detection aims to enhance robustness against attacks that maliciously fail the detection system, including adversarial and backdoor attacks. To address these threats, researchers have developed robust defense strategies, including adversarial feature similarity learning and ensemble methods. By providing an overview of robust detection methods, attack techniques, and defense strategies, this paper highlights the challenges and advancements in creating reliable and generalizable deepfake detection systems.