Robust Deepfake On Unrestricted Media: Generation And Detection
It addresses the social and criminal concerns of deepfakes for the academic and industrial communities, but is incremental as it reviews existing work and suggests future directions.
This chapter examines the evolution and challenges in deepfake generation and detection, aiming to improve detection robustness across diverse media like in-the-wild images and videos, but does not report specific results or numbers.
Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance. Although deepfake media have potential application in a wide range of areas and are drawing much attention from both the academic and industrial communities, it also leads to serious social and criminal concerns. This chapter explores the evolution of and challenges in deepfake generation and detection. It also discusses possible ways to improve the robustness of deepfake detection for a wide variety of media (e.g., in-the-wild images and videos). Finally, it suggests a focus for future fake media research.