Tracing the source of research papers is a fundamental yet challenging task
for researchers. The billion-scale citation relations between papers hinder
researchers from understanding the evolution of science efficiently. To date,
there is still a lack of an accurate and scalable dataset constructed by
professional researchers to identify the direct source of their studied papers,
based on which automatic algorithms can be developed to expand the evolutionary
knowledge of science. In this paper, we study the problem of paper source
tracing (PST) and construct a high-quality and ever-increasing dataset
PST-Bench in computer science. Based on PST-Bench, we reveal several intriguing
discoveries, such as the differing evolution patterns across various topics. An
exploration of various methods underscores the hardness of PST-Bench,
pinpointing potential directions on this topic. The dataset and codes have been
available at this https URL

本文研究了论文来源追踪的问题，并构建了计算机科学领域中高质量且不断增长的数据集 PST-Bench，通过 PST-Bench 揭示了不同主题之间不同的演化模式，并探讨了该领域的潜在方向。

PST-Bench: 追踪和评估出版物的来源

PST-Bench: Tracing and Benchmarking the Source of Publications

Notwithstanding offering convenience and entertainment to society, Deepfake
face swapping has caused critical privacy issues with the rapid development of
deep generative models. Due to imperceptible artifacts in high-quality
synthetic images, passive detection models against face swapping in recent
years usually suffer performance damping regarding the generalizability issue.
Therefore, several studies have been attempted to proactively protect the
original images against malicious manipulations by inserting invisible signals
in advance. However, the existing proactive defense approaches demonstrate
unsatisfactory results with respect to visual quality, detection accuracy, and
source tracing ability. In this study, we propose the first robust identity
perceptual watermarking framework that concurrently performs detection and
source tracing against Deepfake face swapping proactively. We assign identity
semantics regarding the image contents to the watermarks and devise an
unpredictable and unreversible chaotic encryption system to ensure watermark
confidentiality. The watermarks are encoded and recovered by jointly training
an encoder-decoder framework along with adversarial image manipulations.
Extensive experiments demonstrate state-of-the-art performance against Deepfake
face swapping under both cross-dataset and cross-manipulation settings.

该研究提出了第一个强大的身份感知水印框架，可以主动地进行检测和源追踪来对抗 Deepfake 人脸交换，并在不同数据集和处理设置下展示了最先进的性能。