用组合增强提升合成图像检测抗真实世界干扰能力
Composite Data Augmentations for Synthetic Image Detection Against Real-World Perturbations
- 通过遗传算法筛选最优数据增强组合
- 在真实干扰下检测性能提升22.53%平均精度
- 适合需要高鲁棒性的图像真实性验证场景
生成式AI工具的普及使得伪造图像可轻易传播，严重威胁网络信息真实性。现有合成图像检测（SID）方法在经压缩等操作的互联网来源图像上表现不佳。本文通过探索数据增强组合，利用遗传算法优化增强策略，并提出双准则优化方法，在真实世界扰动下显著提升模型性能。最佳模型相比无增强模型，平均精度提升22.53%。研究为应对不同质量与变换的合成图像提供了有效方案，代码已开源。
原文摘要 · Abstract (English)
The advent of accessible Generative AI tools enables anyone to create and spread synthetic images on social media, often with the intention to mislead, thus posing a significant threat to online information integrity. Most existing Synthetic Image Detection (SID) solutions struggle on generated images sourced from the Internet, as these are often altered by compression and other operations. To address this, our research enhances SID by exploring data augmentation combinations, leveraging a genetic algorithm for optimal augmentation selection, and introducing a dual-criteria optimization approach. These methods significantly improve model performance under real-world perturbations. Our findings provide valuable insights for developing detection models capable of identifying synthetic images across varying qualities and transformations, with the best-performing model achieving a mean average precision increase of +22.53% compared to models without augmentations. The implementation is available at github.com/efthimia145/sid-composite-data-augmentation.
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