Facial Deepfake Detection: Five Years of Research Efforts and the FF4ALL-SERICS Experience
Gian Luca Marcialis · 2025
Over the last five years, facial deepfake detection has become one of the most dynamic and challenging areas in multimedia forensics. The scientific community has progressively advanced from early detection attempts to sophisticated models capable of capturing subtle artifacts and inconsistencies in manipulated content. Despite this progress, deepfake forensics remains an arms race: detectors must generalize across unseen generators, remain effective under compression, and resist adversarial manipulations such as morphing and social-media beauty filters. Recent surveys confirm both the maturity of the field and the urgency of addressing robustness, attribution, and lifelong authentication of media. Within this evolving landscape, the sAIfer Lab's Biometric Unit (University of Cagliari) has contributed a coherent body of research, consolidated within the FF4ALL project. Our studies have introduced approaches for artifact decomposition, high-frequency enhancement for compressed content, and tensor-based modeling for scaled and compressed images. We further analyzed fusion rules at score level and generalized detection based on inner/outer face inconsistencies. More recently, we proposed quality-based artifact modeling in videos, evaluated the robustness of forensic tools under morphing and compression, and studied the impact of beauty filters on detection systems. This keynote will reflect on five years of research efforts, combining a broad overview of the state of the art with the experience of our laboratory, and discussing how multidisciplinary collaboration - spanning biometrics, AI, and forensics - can guide the next generation of trustworthy and explainable solutions for deepfake media authentication.