LOTA: Bit-Planes Guided AI-Generated Image Detection

• URL: http://arxiv.org/abs/2510.14230v1
• Date: Thu, 16 Oct 2025 02:12:49 GMT
• Title: LOTA: Bit-Planes Guided AI-Generated Image Detection
• Authors: Hongsong Wang, Renxi Cheng, Yang Zhang, Chaolei Han, Jie Gui,
• Abstract summary: GAN and Diffusion models make it more difficult to distinguish AI-generated images from real ones.<n>We introduce an effective bit-planes guided noisy image generation and exploit various image normalization strategies.<n>Our method achieves an accuracy of over 98.2% from GAN to Diffusion and over 99.2% from Diffusion to GAN.
• Score: 25.736743931612565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of GAN and Diffusion models makes it more difficult to distinguish AI-generated images from real ones. Recent studies often use image-based reconstruction errors as an important feature for determining whether an image is AI-generated. However, these approaches typically incur high computational costs and also fail to capture intrinsic noisy features present in the raw images. To solve these problems, we innovatively refine error extraction by using bit-plane-based image processing, as lower bit planes indeed represent noise patterns in images. We introduce an effective bit-planes guided noisy image generation and exploit various image normalization strategies, including scaling and thresholding. Then, to amplify the noise signal for easier AI-generated image detection, we design a maximum gradient patch selection that applies multi-directional gradients to compute the noise score and selects the region with the highest score. Finally, we propose a lightweight and effective classification head and explore two different structures: noise-based classifier and noise-guided classifier. Extensive experiments on the GenImage benchmark demonstrate the outstanding performance of our method, which achieves an average accuracy of \textbf{98.9\%} (\textbf{11.9}\%~$\uparrow$) and shows excellent cross-generator generalization capability. Particularly, our method achieves an accuracy of over 98.2\% from GAN to Diffusion and over 99.2\% from Diffusion to GAN. Moreover, it performs error extraction at the millisecond level, nearly a hundred times faster than existing methods. The code is at https://github.com/hongsong-wang/LOTA.

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