With the rapid development of generative models, discerning AI-generated
content has evoked increasing attention from both industry and academia. In
this paper, we conduct a sanity check on "whether the task of AI-generated
image detection has been solved". To start with, we present Chameleon dataset,
consisting AIgenerated images that are genuinely challenging for human
perception. To quantify the generalization of existing methods, we evaluate 9
off-the-shelf AI-generated image detectors on Chameleon dataset. Upon analysis,
almost all models classify AI-generated images as real ones. Later, we propose
AIDE (AI-generated Image DEtector with Hybrid Features), which leverages
multiple experts to simultaneously extract visual artifacts and noise patterns.
Specifically, to capture the high-level semantics, we utilize CLIP to compute
the visual embedding. This effectively enables the model to discern
AI-generated images based on semantics or contextual information; Secondly, we
select the highest frequency patches and the lowest frequency patches in the
image, and compute the low-level patchwise features, aiming to detect
AI-generated images by low-level artifacts, for example, noise pattern,
anti-aliasing, etc. While evaluating on existing benchmarks, for example,
AIGCDetectBenchmark and GenImage, AIDE achieves +3.5% and +4.6% improvements to
state-of-the-art methods, and on our proposed challenging Chameleon benchmarks,
it also achieves the promising results, despite this problem for detecting
AI-generated images is far from being solved. The dataset, codes, and pre-train
models will be published at this https URL

通过对于生成模型的 AI 生成图像的检测任务进行检验，本文提出了一个具有挑战性的 Chameleon 数据集和 AIDE 模型，通过多个专家同时提取视觉特征和噪声模式来识别 AI 生成图像，并在现有基准测试以及挑战性的 Chameleon 基准测试中取得了令人满意的结果。

AI 生成图像检测的合理性检查

A Sanity Check for AI-generated Image Detection

The rapid advances in generative AI models have empowered the creation of
highly realistic images with arbitrary content, raising concerns about
potential misuse and harm, such as Deepfakes. Current research focuses on
training detectors using large datasets of generated images. However, these
training-based solutions are often computationally expensive and show limited
generalization to unseen generated images. In this paper, we propose a
training-free method to distinguish between real and AI-generated images. We
first observe that real images are more robust to tiny noise perturbations than
AI-generated images in the representation space of vision foundation models.
Based on this observation, we propose RIGID, a training-free and model-agnostic
method for robust AI-generated image detection. RIGID is a simple yet effective
approach that identifies whether an image is AI-generated by comparing the
representation similarity between the original and the noise-perturbed
counterpart. Our evaluation on a diverse set of AI-generated images and
benchmarks shows that RIGID significantly outperforms existing trainingbased
and training-free detectors. In particular, the average performance of RIGID
exceeds the current best training-free method by more than 25%. Importantly,
RIGID exhibits strong generalization across different image generation methods
and robustness to image corruptions.

该研究提出了一种无需训练的方法（RIGID），通过在视觉基础模型的表示空间中比较原始图像和受到微小噪声扰动的图像之间的相似性，来区分真实图像和由人工智能生成的图像。评估结果显示，RIGID 在各种人工智能生成图像和评估指标上明显优于现有的基于训练和无训练的检测器，尤其是性能平均提高了 25％以上。此外，RIGID 具有很强的泛化能力，适用于不同的图像生成方法，并对图像的损坏具有鲁棒性。

RIGID: 一个面向鲁棒的、无需训练的、模型无关的 AI 生成图片检测框架

RIGID: A Training-free and Model-Agnostic Framework for Robust  AI-Generated Image Detection

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。