In this short consensus paper, we outline risks from upcoming, advanced AI
systems. We examine large-scale social harms and malicious uses, as well as an
irreversible loss of human control over autonomous AI systems. In light of
rapid and continuing AI progress, we propose priorities for AI R&D and
governance.

通过研究先进的人工智能系统，我们指出了即将到来的风险，包括大规模社会伤害和恶意使用，以及人类对自主人工智能系统失去控制的不可逆性。鉴于人工智能的迅速发展，我们提出了人工智能研发和治理的优先事项。

管理快速发展时代的 AI 风险

Managing AI Risks in an Era of Rapid Progress

GAN-generated image detection now becomes the first line of defense against
the malicious uses of machine-synthesized image manipulations such as
deepfakes. Although some existing detectors work well in detecting clean, known
GAN samples, their success is largely attributable to overfitting unstable
features such as frequency artifacts, which will cause failures when facing
unknown GANs or perturbation attacks. To overcome the issue, we propose a
robust detection framework based on a novel multi-view image completion
representation. The framework first learns various view-to-image tasks to model
the diverse distributions of genuine images. Frequency-irrelevant features can
be represented from the distributional discrepancies characterized by the
completion models, which are stable, generalized, and robust for detecting
unknown fake patterns. Then, a multi-view classification is devised with
elaborated intra- and inter-view learning strategies to enhance view-specific
feature representation and cross-view feature aggregation, respectively. We
evaluated the generalization ability of our framework across six popular GANs
at different resolutions and its robustness against a broad range of
perturbation attacks. The results confirm our method's improved effectiveness,
generalization, and robustness over various baselines.

提出了一种基于多视图图像完成表示的鲁棒检测框架，该框架学习各种视图到图像任务来建模真实图像的不同分布，从而稳健和可靠地检测未知的伪造图案，并且针对各种干扰攻击具有鲁棒性。