People now see social media sites as their sole source of information due to
their popularity. The Majority of people get their news through social media.
At the same time, fake news has grown exponentially on social media platforms
in recent years. Several artificial intelligence-based solutions for detecting
fake news have shown promising results. On the other hand, these detection
systems lack explanation capabilities, i.e., the ability to explain why they
made a prediction. This paper highlights the current state of the art in
explainable fake news detection. We discuss the pitfalls in the current
explainable AI-based fake news detection models and present our ongoing
research on multi-modal explainable fake news detection model.

人们现在认为社交媒体是他们唯一的信息来源，但同时社交媒体上假新闻也呈指数级增长。本文强调可解释的假新闻检测在当前技术水平上的现状，并介绍了我们正在开展的多模态可解释假新闻检测模型研究。

通过可解释的 AI 实现智能虚假新闻检测

Towards Smart Fake News Detection Through Explainable AI

The proliferation of fake news and its serious negative social influence push
fake news detection methods to become necessary tools for web managers.
Meanwhile, the multi-media nature of social media makes multi-modal fake news
detection popular for its ability to capture more modal features than uni-modal
detection methods. However, current literature on multi-modal detection is more
likely to pursue the detection accuracy but ignore the robustness of the
detector. To address this problem, we propose a comprehensive robustness
evaluation of multi-modal fake news detectors. In this work, we simulate the
attack methods of malicious users and developers, i.e., posting fake news and
injecting backdoors. Specifically, we evaluate multi-modal detectors with five
adversarial and two backdoor attack methods. Experiment results imply that: (1)
The detection performance of the state-of-the-art detectors degrades
significantly under adversarial attacks, even worse than general detectors; (2)
Most multi-modal detectors are more vulnerable when subjected to attacks on
visual modality than textual modality; (3) Popular events' images will cause
significant degradation to the detectors when they are subjected to backdoor
attacks; (4) The performance of these detectors under multi-modal attacks is
worse than under uni-modal attacks; (5) Defensive methods will improve the
robustness of the multi-modal detectors.

本文针对多媒体假新闻检测方法鲁棒性差的问题，提出了一种系统的鲁棒性评估方法，并通过 5 种对抗攻击和 2 种后门攻击实验验证，结果表明通过防御机制可以提高多模式检测器的鲁棒性。

多模态一定更好吗？多模态假新闻检测的鲁棒性评估

Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection

Existing RGB-D salient object detection (SOD) models usually treat RGB and
depth as independent information and design separate networks for feature
extraction from each. Such schemes can easily be constrained by a limited
amount of training data or over-reliance on an elaborately designed training
process. Inspired by the observation that RGB and depth modalities actually
present certain commonality in distinguishing salient objects, a novel joint
learning and densely cooperative fusion (JL-DCF) architecture is designed to
learn from both RGB and depth inputs through a shared network backbone, known
as the Siamese architecture. In this paper, we propose two effective
components: joint learning (JL), and densely cooperative fusion (DCF). The JL
module provides robust saliency feature learning by exploiting cross-modal
commonality via a Siamese network, while the DCF module is introduced for
complementary feature discovery. Comprehensive experiments using five popular
metrics show that the designed framework yields a robust RGB-D saliency
detector with good generalization. As a result, JL-DCF significantly advances
the state-of-the-art models by an average of ~2.0% (max F-measure) across seven
challenging datasets. In addition, we show that JL-DCF is readily applicable to
other related multi-modal detection tasks, including RGB-T (thermal infrared)
SOD and video SOD, achieving comparable or even better performance against
state-of-the-art methods. We also link JL-DCF to the RGB-D semantic
segmentation field, showing its capability of outperforming several semantic
segmentation models on the task of RGB-D SOD. These facts further confirm that
the proposed framework could offer a potential solution for various
applications and provide more insight into the cross-modal complementarity
task.

通过 Siamese 架构的 RGB-D 联合学习和密集协同融合 (JL-DCF) 架构，本文提出了两个有效的组件：联合学习和密集的协同融合。综合实验表明，该方案在七个具有挑战性的数据集上将平均 F 值提高了～2.0%，并且在相关的多模态检测任务中也取得了相当甚至更好的性能。