通过计算梯度捕捉像素波动，高效识别合成图像。
Minimalist Preprocessing Approach for Image Synthesis Detection
- 利用相邻像素梯度检测灰度变化，实现轻量级特征提取
- 在多个数据集上达到与顶尖方法相当的准确率
- 计算开销极低，适合手机等低配设备部署
生成模型显著提升了图像合成能力，使合成图像越来越接近真实图像。然而，恶意制造虚假图像的问题日益严重。低配置智能设备普及，使得欺骗性图像更易传播。因此，亟需高效的检测方法。本文提出一种简单而高效的方法：通过计算像素梯度捕捉邻近像素间的灰度强度波动，该方法起到高通滤波作用，突出关键区分特征，同时最小化色彩干扰。实验表明，在多个数据集上，该方法性能接近当前最优水平，且计算资源消耗极低，适用于智能手机等低端设备部署。代码已开源：https://github.com/vohoaidanh/adof。
原文摘要 · Abstract (English)
Generative models have significantly advanced image generation, resulting in synthesized images that are increasingly indistinguishable from authentic ones. However, the creation of fake images with malicious intent is a growing concern. Low-configured smart devices have become highly popular, making it easier for deceptive images to reach users. Consequently, the demand for effective detection methods is increasingly urgent. In this paper, we introduce a simple yet efficient method that captures pixel fluctuations between neighboring pixels by calculating the gradient, which highlights variations in grayscale intensity. This approach functions as a high-pass filter, emphasizing key features for accurate image distinction while minimizing color influence. Our experiments on multiple datasets demonstrate that our method achieves accuracy levels comparable to state-of-the-art techniques while requiring minimal computational resources. Therefore, it is suitable for deployment on low-end devices such as smartphones. The code is available at https://github.com/vohoaidanh/adof.
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