Minimalist Preprocessing Approach for Image Synthesis Detection

2026-06-23

AI Summary

This work addresses the growing security risks posed by the proliferation of generative models on resource-constrained smart devices by proposing a lightweight and efficient method for detecting synthetic images. The approach employs a minimal preprocessing pipeline based on pixel-wise gradient computation, leveraging high-pass filtering to emphasize local grayscale intensity variations—identified as a key discriminative cue—while simultaneously suppressing color-related artifacts. Without relying on complex architectures or substantial computational resources, the proposed paradigm achieves detection accuracy comparable to state-of-the-art methods across multiple benchmark datasets, yet with significantly reduced computational overhead, thereby enabling real-time deployment on devices such as smartphones.

Abstract

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.

Problem

• image synthesis detection
• generative models
• fake image detection
• low-resource devices
• image forensics

Innovation

• image synthesis detection
• gradient-based preprocessing
• high-pass filtering
• lightweight detection
• pixel fluctuation