The recent development of generative models unleashes the potential of
generating hyper-realistic fake images. To prevent the malicious usage of fake
images, AI-generated image detection aims to distinguish fake images from real
images. Nevertheless, existing methods usually suffer from poor
generalizability across different generators. In this work, we propose an
embarrassingly simple approach named SSP, i.e., feeding the noise pattern of a
Single Simple Patch (SSP) to a binary classifier, which could achieve 14.6%
relative improvement over the recent method on GenImage dataset. Our SSP method
is very robust and generalizable, which could serve as a simple and competitive
baseline for the future methods.

为了防止虚假图像的恶意使用，本研究提出了一种简单且具有鲁棒性和通用性的方法，即将单个简单贴片的噪声模式馈入二元分类器，相对于最新方法在 GenImage 数据集上达到 14.6% 的相对改进，可以作为未来方法的简单而有竞争力的基准。

AI 生成图像检测所需的单个简单补丁

A Single Simple Patch is All You Need for AI-generated Image Detection

Spoken semantic parsing (SSP) involves generating machine-comprehensible
parses from input speech. Training robust models for existing application
domains represented in training data or extending to new domains requires
corresponding triplets of speech-transcript-semantic parse data, which is
expensive to obtain. In this paper, we address this challenge by examining
methods that can use transcript-semantic parse data (unpaired text) without
corresponding speech. First, when unpaired text is drawn from existing textual
corpora, Joint Audio Text (JAT) and Text-to-Speech (TTS) are compared as ways
to generate speech representations for unpaired text. Experiments on the STOP
dataset show that unpaired text from existing and new domains improves
performance by 2% and 30% in absolute Exact Match (EM) respectively. Second, we
consider the setting when unpaired text is not available in existing textual
corpora. We propose to prompt Large Language Models (LLMs) to generate unpaired
text for existing and new domains. Experiments show that examples and words
that co-occur with intents can be used to generate unpaired text with Llama
2.0. Using the generated text with JAT and TTS for spoken semantic parsing
improves EM on STOP by 1.4% and 2.6% absolute for existing and new domains
respectively.

使用现有和新领域的非配对文本可以提高已有和新领域的口语语义解析的性能，同时，通过提示大型语言模型生成非配对文本，可以进一步提升性能。