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。