论文标题
在不受限制的媒体上强大的深层捕捞：生成和检测
Robust Deepfake On Unrestricted Media: Generation And Detection
论文作者
论文摘要
深度学习的最新进展导致了Deepfake的产生的实质性改善，从而使假媒体具有更现实的外观。尽管DeepFake媒体在广泛的领域中具有潜在的应用，并且引起了学术和工业社区的广泛关注，但它也引起了严重的社会和犯罪问题。本章探讨了深泡产生和检测中挑战的演变和挑战。它还讨论了改善各种媒体（例如野外图像和视频）的深泡检测稳健性的可能方法。最后，它建议将来的假媒体研究重点。
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.
