融合CNN与ViT提升AI生成图像识别准确率
AI-Generated Image Recognition via Fusion of CNNs and Vision Transformers
- 结合CNN与视觉Transformer的多模型融合策略
- 在CIFAKE数据集上达到97.32%识别准确率
- 适合关注AI图像检测与数据可信验证的研究者
近年来，合成数据技术的进步使得高质量图像生成成为可能，模糊了真实图像与AI生成图像之间的界限。这一趋势对数据可靠性与真实性构成严峻挑战，亟需可靠的检测方法。本文提出一种基于多检测方法融合的鲁棒识别方案，充分利用CNN与视觉Transformer的优势。在CIFAKE数据集上的大量实验表明，该模型表现优异，识别准确率达到97.32%。结果证明该方法能有效区分AI生成图像与真实图像，为合成数据泛滥背景下的数据认证技术发展提供了有力支持。
原文摘要 · Abstract (English)
Recent advancements in synthetic data technology have opened a new era where images of remarkable quality are generated, blurring the lines between real-life images and those produced by Artificial Intelligence (AI). This evolution poses a significant challenge to ensuring the reliability and authenticity of data, underscoring the need for robust detection methods. In this paper, we present a robust approach aimed at addressing these pressing concerns. Our methodology revolves around leveraging fusion strategies, combining the strengths of multiple detection methods for identifying AI-generated images. Through extensive experimentation on the CIFAKE dataset, our model showcases remarkable performance, achieving an impressive accuracy rate of 97.32%. This accomplishment underscores the efficacy of our approach in accurately distinguishing between AI-generated images and real-life images, thus contributing to the advancement of data authentication techniques amidst the proliferation of synthetic data.
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