Academic Journal of Engineering and Technology Science, 2026, 9(3); doi: 10.25236/AJETS.2026.090309.
Christina Yanlin Chiu1, Andrew Chiu2, Xu Xiaorui3, Chen Ziyi4, Fun Ming Yang5
1Wyoming Seminary, Kingston, USA
2Wyoming Seminary, Kingston, USA
3Basis International School, Shenzhen, China
4Jinan Thomas School, Jinan, China
5St Michaels University School, British Columbia, Canada
The rapid advancement of generative artificial intelligence has brought significant convenience to image processing but has also led to a surge in AI-forged images. In e-commerce, malicious actors exploit these tools to fabricate "damaged goods" images for refund fraud. To address this, we propose an efficient image forensics framework based on generative models to identify and trace AI-forged images. By integrating reverse forensics of generative models, this framework leverages a "magic defeats magic" approach. We introduce feature trajectory prediction and multimodal feature fusion to enhance the detection of subtle forgery traces in low-quality images. Furthermore, an efficient batch detection system using a fast-screening and fine-detection cascade is developed to meet the real-time processing demands of large-scale e-commerce platforms. The framework provides not only binary classification but also explainable forensics via heatmaps and frequency anomaly visualizations. Our approach demonstrates strong robustness and high accuracy, offering a scalable technical path for cross-domain applications including social media content verification, judicial authentication, and copyright protection.
AI image forensics, Generative models, Feature trajectory, Multimodal fusion, E-commerce fraud
Christina Yanlin Chiu, Andrew Chiu, Xu Xiaorui, Chen Ziyi, Fun Ming Yang. Generative-Model-Based AI Image Forgery Forensics and Efficient Detection Framework. Academic Journal of Engineering and Technology Science (2026), Vol. 9, Issue 3: 66-70. https://doi.org/10.25236/AJETS.2026.090309.
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