Deceptive images can be shared in seconds with social networking services,
posing substantial risks. Tampering traces, such as boundary artifacts and
high-frequency information, have been significantly emphasized by massive
networks in the Image Manipulation Localization (IML) field. However, they are
prone to image post-processing operations, which limit the generalization and
robustness of existing methods. We present a novel Prompt-IML framework. We
observe that humans tend to discern the authenticity of an image based on both
semantic and high-frequency information, inspired by which, the proposed
framework leverages rich semantic knowledge from pre-trained visual foundation
models to assist IML. We are the first to design a framework that utilizes
visual foundation models specially for the IML task. Moreover, we design a
Feature Alignment and Fusion module to align and fuse features of semantic
features with high-frequency features, which aims at locating tampered regions
from multiple perspectives. Experimental results demonstrate that our model can
achieve better performance on eight typical fake image datasets and outstanding
robustness.

社交网络服务下的欺诈图像存在重大风险，我们提出了一个新的 Prompt-IML 框架来使用预训练的视觉基础模型辅助图像操作定位，所设计的模型在八个典型虚假图像数据集上表现出更好的性能和卓越的鲁棒性。

PROMPT-IML：通过 Prompt 调整预训练的基础模型实现图像操作定位

PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation  Models Through Prompt Tuning

Numerous forms of consumer-generated media (CGM), such as social networking
services (SNS), are widely used. Their success relies on users' voluntary
participation, often driven by psychological rewards like recognition and
connection from reactions by other users. Furthermore, a few CGM platforms
offer monetary rewards to users, serving as incentives for sharing items such
as articles, images, and videos. However, users have varying preferences for
monetary and psychological rewards, and the impact of monetary rewards on user
behaviors and the quality of the content they post remains unclear. Hence, we
propose a model that integrates some monetary reward schemes into the SNS-norms
game, which is an abstraction of CGM. Subsequently, we investigate the effect
of each monetary reward scheme on individual agents (users), particularly in
terms of their proactivity in posting items and their quality, depending on
agents' positions in a CGM network. Our experimental results suggest that these
factors distinctly affect the number of postings and their quality. We believe
that our findings will help CGM platformers in designing better monetary reward
schemes.

利用消费者生成的媒体平台进行实验，发现使用不同形式的货币奖励方案对用户在平台上发布内容的数量和质量产生不同的影响。

消费者生成媒体中的用户基于位置的策略与货币奖励

User's Position-Dependent Strategies in Consumer-Generated Media with  Monetary Rewards

Consumer generated media (CGM), such as social networking services rely on
the voluntary activity of users to prosper, garnering the psychological rewards
of feeling connected with other people through comments and reviews received
online. To attract more users, some CGM have introduced monetary rewards (MR)
for posting activity and quality articles and comments. However, the impact of
MR on the article posting strategies of users, especially frequency and
quality, has not been fully analyzed by previous studies, because they ignored
the difference in the standpoint in the CGM networks, such as how many
friends/followers they have, although we think that their strategies vary with
their standpoints. The purpose of this study is to investigate the impact of MR
on individual users by considering the differences in dominant strategies
regarding user standpoints. Using the game-theoretic model for CGM, we
experimentally show that a variety of realistic dominant strategies are evolved
depending on user standpoints in the CGM network, using multiple-world genetic
algorithm.

本研究旨在探讨在用户立场的差异下，Monetary Rewards 对文章发布策略的影响。我们使用多重世界遗传算法，通过 CGM 游戏理论模型的实验，证明了用户在 CGM 网络中会出现多种具有现实意义的主导策略。

货币奖励对使用协同进化学习的用户个体策略的影响

Effect of Monetary Reward on Users' Individual Strategies Using  Co-Evolutionary Learning

Social networking services have became an important communication channel in
time of emergency. The aim of this study is to create a machine learning
language model that is able to investigate if a person or area was in danger or
not. The ubiquitousness of smartphones enables people to announce an emergency
they are observing in real-time. Because of this, more agencies are interested
in programmatically monitoring Twitter (i.e. disaster relief organizations and
news agencies). Design a language model that is able to understand and
acknowledge when a disaster is happening based on the social network posts will
become more and more necessary over time.

利用机器学习语言模型对社交网站的帖子进行监测，以便能够及时地确定是否有人或地区处于危险之中，从而更好地进行应急处置。